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Ten31 Signal Engine — Pilot Backtest Write-up
Author: Claude (Claude Code), implementing dev For: Grant + the dev who authored the handoff/scoping document Date: 2026-06-08 Status: Pilot build complete; §7.1 backtest executed end-to-end with a qualified result. This document is the honest assessment, the judgment calls I made, and the open questions for a second opinion.
Read this as a peer review request, not a victory lap. The engine works end-to-end and surfaced the right thesis, but the signal quality on the current corpus is coarse, and several design tensions in the handoff doc only became visible once there was real data flowing through. Those tensions — especially the cross-cluster gating question Grant raised — are the point of this write-up.
1. Executive summary
I built the full pilot per the handoff: ingestion (audio + text) → local claim extraction → hybrid vector store → the "scoring brain" (independence-discounted, as-of-disciplined nomination) → the §7.1 backtest → a dual-evaluation ledger. It runs against the operator's real local-compute stack (Spark Control) and a real ~6,600-claim corpus drawn from ~25 companies and a handful of podcasts.
The §7.1 backtest verdict is a qualified YES. Seeded with the 2023 Kirkwood "power is the binding constraint" conviction and marched as-of across 2023–2024, the under-acted-conviction scorer:
- surfaced the root thesis cross-cluster in May 2023 (energy and AI sources, independent), and
- surfaced the headline derivative ("size up the power-infra picks-and-shovels") in May 2024, along with transformers and utilities-repriced.
So the mechanism the project exists to build — fan a held conviction to its derivatives and catch the world starting to corroborate them — demonstrably works on real history.
But three honest caveats keep it from being a clean win, and they drive the open questions:
- The signal is noisy (the acceleration metric swings between earnings seasons; there's visible run-to-run variance).
- The cross-cluster breadth shows up at the root level, not the derivative level — the specific power-infra derivatives stay energy-cluster-corroborated.
- The derivatives only clear because I relaxed a cross-cluster gate for Job B — a judgment call (§7 below) that is exactly what Grant wants to debate.
The most important open question, in Grant's words: is strict cross-cluster gating limiting our ability to pick up signal early — and is the real fix to dramatically broaden the cluster taxonomy and the corpus? I think the answer is largely yes, and I lay out why in §8.
2. What was built (architecture as implemented)
3,347 lines of Python, 44 modules. Everything local-compute runs through the operator's existing Spark Control gateway (we call HTTP endpoints; we did not stand up vLLM/Whisper/Qdrant). The one external call is the bounded frontier step (not exercised in the backtest — see §7, deferred).
| Layer (handoff §) | What's built | Notes |
|---|---|---|
| Ingestion — text (§4.1) | SEC EDGAR (10-K/10-Q/20-F/40-F), FMP earnings-call transcripts | Earnings-call audio proved unfetchable (no uniform feed, ~30–90d replay expiry) → FMP transcript API, per §12. Filings dedup on accession; earnings on symbol+quarter. |
| Ingestion — audio (§4.1, §4.5) | RSS + YouTube fetch, long-audio chunking (~2.5 min), Parakeet transcribe + Sortformer diarize + 192-d TitaNet voiceprints, cross-chunk speaker stitching, a persisted voiceprint library | Verified live: a real podcast → speaker-attributed transcript → claims. |
| Speaker identity (§4.5) | Voiceprint cosine matching across episodes/shows + LLM speaker-naming (host/guest from the intro) → name-based independence edges | Grant's idea: name-based overlap is robust to voiceprint drift across shows. Both edge types feed the independence graph. |
| Extraction (§4.2) | Local Qwen, the finalized claim schema, JSON-mode, temp 0, "willing to emit zero" | Pluggable backend: local Qwen (default) or Gemini batch (validated, for overflow/scale; public corpus only). |
| Embedding + store (§4.3) | bge-m3 dense + BM25 sparse → Qdrant hybrid collection; retrieval + rerank via the gateway | Embeds distilled propositions, not raw chunks. |
| Scoring brain (§4.4, §4.5) | EISC independence primitive; as-of harness; windowed acceleration; under-acted-conviction (Job B) scorer; the quantitative bar; ledger writer; resolver (stub) | See §3. Job A scorers (emergence/stance/intersection) and the frontier judge/fan-out are deferred per the blueprint build-order — the backtest is Job B only. |
| Backfill queue (§13.4) | Client-side GPU-hours queue: idempotent, leased/crash-safe, prioritized | Extraction ran ~900 docs on one GPU as a serial job. Transcription on the other GPU in parallel. |
| Provenance / dedup | Layered: stable item-id (robust pre-GPU guard) + normalized title/date (cross-mirror) + content-hash (audit only) | Corrected after Grant flagged that a transcript hash is a brittle dedup key. |
| Ledger (§4.7, §6) | SQLite dual-evaluation ledger; logs every bar-clearer; resolution columns separated from scoring (look-ahead guard) | Live with its first entries. |
| UI | FastAPI corpus-management app (dashboard, add/view sources, inspect per-source claims) | The "menu" to grow and audit the corpus over time. |
Corpus the backtest ran on (snapshot): 6,569 claims (5,129 embedded at backtest time), from 411 filings + 410 earnings transcripts + 82 podcast episodes (4 RSS-full shows for 2022–2023: Dwarkesh, Hidden Forces, All-In, Invest Like the Best; plus a partial Catalyst slice). Claim types: 2,780 predictive / 1,447 interpretive / 2,267 descriptive / 75 reactive. Clusters: energy 3,135 · ai_tech 2,329 · bitcoin 765 · vc_consensus 139 · macro 103 · generalist 98. 90 voiceprints (35 named), 10 shared-guest edges.
Note the cluster imbalance — it's central to §8. The corpus is overwhelmingly company filings/earnings (two clusters, energy + ai_tech) with a thin podcast layer. That is not a balanced cross-cluster corpus.
3. The scoring brain (how nomination works)
This is the part where the handoff's hard constraints (§5) had to become concrete code. Design was done via a 3-way design panel (statistical / graph / pragmatic lenses) synthesized into one blueprint; I then built it.
- EISC — Effective Independent Source Count (the §4.5 differentiator). Given the sources converging on a topic, discount by connectedness using a noisy-OR connectedness matrix + inverse-row-sum. Verified on synthetic cases: 5 identical clones → ~1.0 voice; 5 cross-cluster independents → ~5.0; all-bitcoin → floored ~0.4; "one guest doing the rounds" across many shows → ~1.0. (I improved the cross-cluster multiplier over the blueprint so a single guest spanning many clusters can't fake the gold-tier bonus.) Every count that feeds a score routes through EISC — never a raw source count.
- As-of harness (§6.6). Every scorer reads an
as_of-filtered view; nothing reads the raw claims table. At nomination time only claims dated ≤ as_of are visible. This is what makes the backtest honest (no look-ahead). - Windowed acceleration (§4.4). The signal is the discrete 2nd derivative of the EISC-weighted claim flow per topic — not raw size. Window length must match corpus cadence (90 days for quarterly filings; 28 for weekly podcasts).
- Under-acted-conviction / Job B (§4.4).
conviction_weight × exposure_gap × rising_independent_corroboration. Corroboration = retrieve (hybrid search) → LLM filter to affirms-only → independence-weighted acceleration over the confirmed set. Exposure is joined locally and never crosses the frontier boundary (§4.6). - The quantitative bar (§5.1). Two tiers: an evidence bar (clears hard gates → log a ledger row, the denominator) and a promotion bar (also clears a score threshold → would go to the frontier judge). Stats nominate; the model would only judge a pre-filtered shortlist.
4. The §7.1 backtest — methodology
Per the handoff (§7.1 is the headline pilot test), I ran it before any forward pilot.
- Seed: the 2023 Kirkwood conviction
K2023("compute will ~1000x; energy becomes the binding constraint; interruptible load is the edge"), logged in the human-owned conviction log with high conviction / low exposure (lt2). - Fan-out (v1, hand-written): Per the blueprint's build order, I hand-wrote the 2nd/3rd-order derivative tree (grid interconnect, transformers, substations, cooling, gas turbines, nuclear, uranium, utilities repriced, and the headline "size up power-infra picks-and-shovels"). Why hand-written: it removes the frontier from the first backtest and isolates the real question — does the scoring surface the derivative once it exists? — from the separate question of whether the frontier can propose the right derivatives. (That second question is untested; see §6.)
- Run: marched a quarterly
as_offrom 2023-03 to 2024-09 (7–9 points), 90-day windows. At each as_of, for each derivative: retrieve corroboration from the corpus, LLM-filter to genuine affirmations, compute independence-weighted acceleration, apply the bar, log every clearer to the ledger. - Look-ahead control: all retrieval/scoring at as_of only sees claims dated ≤ as_of. The resolver (forward leg) is a separate, isolated pass (a stub for now — see §6).
5. The §7.1 backtest — results
I ran it twice: once on the company-only corpus (~4,500 claims), then a "sharpened" re-run after the cross-cluster podcast claims landed (~5,100 embedded). Presenting both is deliberate — the differences between them are themselves a finding (run-to-run variance / noise).
Run 1 — company corpus (~4,500 claims)
| Derivative | First cleared evidence bar | Evidence at clear |
|---|---|---|
| Root: "power is the binding constraint" | 2023-05-30 | EISC 3.0, 4 sources, k_eff=2 (cross-cluster: energy+AI), accel +1.0 |
| Headline: "picks-and-shovels" | 2024-05-24 | EISC 2.0, 5 sources, k_eff=1, score 2.56 |
| Utilities repriced | 2024-05-24 | EISC 2.5, 8 sources, k_eff=1, built steadily from 2023 (src 1→2→4→8) |
| nuclear / transformers / gas / uranium / cooling | peaked but did not clear | EISC or acceleration fell short in the cleared window |
Run 2 — + cross-cluster podcast claims (~5,100 embedded)
| Derivative | First cleared | Note |
|---|---|---|
| Root | 2023-05-30 | unchanged (cross-cluster) |
| Headline: "picks-and-shovels" | 2024-05-24 | peak 3.33; notably it scored 3.33 back at 2023-11 but EISC was 1.6, just under the 2.0 floor, so it logged-but-didn't-clear then |
| Transformers | 2024-05-24 | newly cleared (peak 4.80) |
| Uranium | did not clear | peak 7.04 (!) but never simultaneously cleared all gates |
| Utilities repriced | did not clear | cleared in Run 1, not in Run 2 — this is the run-to-run variance / noise, exhibited directly |
What the numbers say, honestly:
- The root thesis is a genuinely clean result — it cleared cross-cluster (k_eff=2) in May 2023 in both runs, independent of the contested design call. The system would have flagged "the world is starting to corroborate that power is the binding constraint, and Ten31 is under-exposed" in mid-2023.
- The derivatives surface, but messily. They clear mid-2024, mostly single-cluster, and which ones clear shifts between runs. The acceleration (2nd derivative) flips sign between earnings seasons (
+2.6 → −2.2 → +1.6 → −1.0), so a derivative clears in whatever window the curvature happens to be positive. That is fragile.
6. Honest assessment
What worked well
- The end-to-end machine is real and disciplined. Ingest (text and audio) → local extraction → hybrid store → independence-discounted nomination → as-of-honest backtest → ledger. It runs on the operator's actual stack, on a real multi-thousand-claim corpus.
- The EISC independence primitive does its job. "Five shows, one guest" collapses to ~1 voice; the bitcoin cluster is structurally floored; cross-cluster gets the bonus. This is the heart of §4.5 and it behaves correctly and auditably (every score is reconstructable from its inputs).
- Extraction discipline holds. The extractor emits zero on boilerplate (8-Ks, 10-K front-matter) and rich, well-typed claims on earnings Q&A (~82% interpretive/predictive vs. descriptive). Earnings calls massively out-yield filings for signal — a concrete finding that confirms a §4.1 hypothesis.
- The root-thesis result is the real validation. The single most important thing §7.1 asked — would the engine have surfaced this in time — is yes for the root conviction, cross-cluster, in 2023.
- The as-of discipline + the ledger are correct by construction. Resolution is structurally separated from scoring; the denominator started day one; the model never sees a human rating before logging. The anti-self-deception machinery is in place.
Limitations & open questions (the important half)
- Noise on sparse, quarterly, single-domain data. The 2nd-derivative acceleration is fragile when claims cluster in earnings seasons. The blueprint deliberately deferred the statistical smoothing (weighted-quadratic fits, significance gates, shrinkage) as premature at small n. Open question: with a bigger corpus, is raw 2nd-difference enough, or do we need that smoothing now? The run-to-run variance suggests we need something.
- Cross-cluster breadth is at the root, not the derivatives. The diagnosis was concrete: in 2022–2023, AI-company earnings barely mentioned electricity as a constraint (that narrative hit 2024–25). So the niche power-infra derivatives are corroborated almost entirely by the energy cluster. The cross-domain early discussion lived in specialist discourse (energy/macro podcasts), which we under-sampled. This is the crux — see §8.
- The frontier fan-out is untested. The backtest used a hand-written derivative tree. We have not validated whether the frontier model, given the seed conviction, would propose the right derivatives (grid/transformers/nuclear/…). That's a separate and important test (it's the other half of Job B). It's deferred, not done.
- No lead-time measured yet. The resolver (external-confirmation leg) is a stub. We can say the engine surfaced the derivatives at specific dates, but we have not yet measured earliness against the actual repricing of power infrastructure (the alpha measurement, §6.3). That needs price/event data and forward time.
- Filing extraction targets the wrong thing. It reads filings front-to-back; 10-K front-matter and risk-factors are low-yield. It should target Item 7 (MD&A). This skews filing claims toward boilerplate and likely costs us signal.
- Stance/relation extraction is thin. The local extractor sees one chunk at a time, so it rarely wires the cross-document
relationlinks the §4.2 schema assumes. The Job A contrarian scorer therefore needs a separate LLM stance-folding pass (designed, not built). Worth flagging to the handoff author: the schema implies relation-linking that is hard to populate at extraction time.
7. Judgment calls I made (please scrutinize all of these)
Every place I made a decision the handoff didn't fully specify, or where I diverged:
- [BIGGEST] Relaxed the cross-cluster gate for Job B. The design blueprint applied the §4.5 cross-cluster rule (
k_eff ≥ 2) as a universal hard gate. I removed it as a hard gate for the under-acted-conviction (Job B) scorer — keeping EISC ≥ 2.0 (genuine independence) and a ≥2-source requirement, and letting cross-cluster boost the score instead of gating it. Rationale: the handoff §4.4 defines Job B as "rising independent corroboration," whereas §4.5's cross-cluster-is-gold framing is about Job A discovery (avoiding echo chambers). N independent energy companies confirming a power thesis is corroboration, not an echo. This is the difference between the derivatives clearing or not — with the strict gate, only the root clears (cross-cluster, 2023). This is the #1 thing to debate (§8). - Window length = 90 days for the backtest (blueprint default was 28). 28-day windows are degenerate on quarterly filings/earnings (most windows empty). Made it configurable; 90d for filing-cadence corpora, 28d for weekly podcasts. Open question: mixed-cadence corpora (filings + podcasts) want different windows simultaneously — currently one global value.
- Improved the EISC cross-cluster multiplier. Blueprint counted "distinct non-capped clusters present." I changed it to count only clusters that contribute ≥ 0.5 of an independent voice — so "one guest spanning 4 clusters" can't earn the gold multiplier. (A correctness fix, not a divergence in intent.)
- Hand-wrote the fan-out for v1 (per blueprint build-order). The derivative phrasings are mine, and the LLM relevance filter judges corroboration against those phrasings — so wording matters. A frontier-generated tree might phrase them to match the corpus better (or worse). Untested.
- Deferred the statistical-significance machinery (Design 1's fitted curves / bootstraps / z-gates) as premature at pilot n — kept the hard minimum-evidence gates, not the smoothing. This is why the signal is noisy. Reconsider as the corpus grows (§6.1).
- Build order: Job B first; Job A (emergence/stance/intersection) and the frontier judge/fan-out deferred. So the backtest tested Job B only, with no frontier in the loop. Faithful to the blueprint, but it means large parts of the §4 design are designed-not-built.
- Filings = 10-K/10-Q/20-F/40-F only (skipped 8-K/6-K as low-yield current-reports). Earnings via FMP. Podcasts = the 4 RSS-full shows + a partial Catalyst slice. I did not get the specialist energy/macro podcasts (Catalyst/Columbia Energy/Macro Voices/Odd Lots) for 2022–2023 — they're YouTube-only with slow date-windowed enumeration. This under-samples exactly the cluster breadth the derivatives needed.
- Local Qwen for all extraction + scoring LLM helpers. Gemini validated as an overflow backend but not used in the backtest.
8. The central debate: cross-cluster gating vs. corpus breadth
This is the section to take into the brainstorm. Grant's framing (paraphrased): strict cross-cluster gating may limit our ability to pick up signal early; perhaps the real fix is that the cluster list is too small and there isn't enough breadth within each cluster, so the corpus needs to be dramatically increased. I think this is the right instinct, and here's the structured case.
The tension, precisely
- §4.5 is unambiguous and correct for Job A discovery: cross-cluster convergence is gold, within-cluster is near-noise (five bitcoin shows agreeing = the prior, not signal).
- But Job B (derivatives / fan-out) has the opposite early-signal dynamic. A niche derivative's earliest corroboration almost always comes from the single most-relevant cluster — the people closest to it. Power-infra repricing showed up first in energy-company earnings and energy-specialist discourse, and only later spread to AI companies and generalist macro. Requiring cross-cluster corroboration means you only fire once the signal has already spread — which is precisely when you've lost the lead time. The backtest demonstrates this exactly: the cross-cluster version of the signal (the root) is real but broad; the actionable derivative corroboration is single-cluster and earlier.
This is, I think, a genuine gap in the handoff: §4.5's "within-cluster is near-noise" was written with discovery in mind and is in tension with §4.4's "rising independent corroboration" for Job B. The implementation had to pick; I picked "relax for Job B." The dev who wrote the spec should weigh in on whether that's the intended reading.
Why this points at corpus breadth (Grant's hypothesis), and I agree
The reason single-cluster corroboration feels uncomfortable is the fear of an echo chamber (energy companies talking their book). The principled fix isn't to demand cross-cluster — it's to make "independent within a domain" mean something, which requires breadth. Right now:
- We have 6 coarse clusters, and the corpus is dominated by two of them (energy, ai_tech), almost entirely company filings/earnings. Within "energy," CEG/VST/TLN/NEE are independent issuers but they're all sell-side-of-their-own-demand — partly correlated by construction.
- A handful of podcasts (4 shows) provide the only non-company voices, and the specialist energy/macro podcasts that would carry the early cross-domain signal weren't ingested for the backtest window.
So the corpus is both too narrow (few clusters, two dominant) and too shallow within clusters (few genuinely independent voice-types per cluster). Two complementary directions:
- Finer cluster taxonomy. "Energy" → {power utilities, grid/equipment, nuclear/uranium, gas, energy-specialist media}. "AI/tech" → {chips, hyperscalers, data-center REITs, AI-specialist media}. Add clusters the pilot omitted entirely: sell-side research, trade press / industry newsletters, expert-network transcripts, specialist substacks, conference/earnings-adjacent commentary, policy/regulatory. With a finer taxonomy, cross-sub-cluster convergence (e.g., a nuclear operator and a grid-equipment maker and an energy-trade newsletter) becomes a meaningful early signal — and the strict cross-cluster gate becomes defensible again because the clusters are now granular enough to convergence early.
- Dramatically more breadth within each cluster. More issuers, far more podcasts/media, and crucially the specialist sources where derivatives are discussed first. This is the difference between "4 energy companies" (correlated) and "20 independent energy-ecosystem voices of different types" (genuinely independent).
My recommendation for the debate (not a decision — a starting position)
- Short term: keep Job B's gate at independence (EISC ≥ 2, ≥2 sources) for the evidence/logging tier — so we catch and log early single-cluster corroboration and start the lead-time clock — and use cross-cluster as the promotion/confidence tier (the thing we'd actually act on). This preserves earliness and honesty: we log the early single-cluster whisper, but we don't treat it as high-confidence until it's broadened.
- Medium term (the real fix, Grant's point): broaden the cluster taxonomy and dramatically expand the corpus — especially the specialist/media sources and finer sub-clusters. This likely does more for signal quality than any scoring tweak, and it would let us re-tighten the cross-cluster requirement without losing earliness, because convergence would happen earlier across a richer cluster space.
- Either way: build the resolver / lead-time measurement next, because "did it clear the bar" is far less interesting than "how early did it clear vs. the actual repricing" — and that number is what tells us whether the relaxed gate is finding alpha or just noise.
9. Suggested agenda for the brainstorm with the handoff author
- The §4.4-vs-§4.5 tension for Job B. Is "rising independent corroboration" meant to allow single-cluster (independent-within-domain) corroboration, with cross-cluster as a confidence multiplier? Or is cross-cluster a hard requirement even for derivatives (accepting later signal)? This is the load-bearing question.
- Cluster taxonomy + corpus breadth. How far to broaden clusters and sources? Which new source types matter most (sell-side, trade press, expert networks, specialist media)? What's the target corpus size for the cross-cluster signal to be early and honest?
- The temporal statistic. Is raw 2nd-difference acceleration the right signal, or do we adopt the deferred smoothing now? The run-to-run variance argues for the latter.
- Frontier fan-out validation. Design a test for whether the frontier proposes the right derivatives from a seed conviction (the untested half of Job B).
- Lead-time / resolution. What external-confirmation data (price, signed deals, policy) feeds the resolver, and how do we grade earliness?
- Filing extraction → MD&A targeting, and the relation/stance extraction gap (does the §4.2 schema's relation-linking need a dedicated pass?).
10. Appendix
Corpus at backtest time: 6,569 claims (5,129 embedded) · 411 filings + 410 earnings + 82 podcasts + 3 youtube · 47 sources · 90 voiceprints (35 named) · 10 shared-guest edges · 4 ledger rows · 81 candidate-score rows.
Key parameters: windows 90d × 3 (84/270-day lookback); EISC floor 2.0; under-acted score floor 0.3; coupling κ {shared_guest 0.85, citation 0.45, community 0.60}; cluster coupling {bitcoin 0.55, vc_consensus 0.35, other-same 0.25}; bitcoin/capped contribution ≤ 0.25.
The contested gate, in code: signal_engine/signals/bar.py::_under_acted — the k_eff ≥ 2 requirement is commented out with the rationale; re-adding it reverts to "only the root clears."
Reproduce: python -m signal_engine backtest --conviction K2023 --start 2023-03-01 --end 2024-09-01 --step-days 90 --window-days 90. Trajectories print per-derivative with the evidence at each as_of.
Module map: ingest/ (fetch + transcribe + diarize + identify), extract/ (claims + backends), embedstore/ (Qdrant hybrid), signals/ (the scoring brain: independence, asof, windows, under_acted, bar, ledger_writer, resolver, run), frontier/ (designed, deferred), spark/ (the single gateway client), store/ (schema + seeds), ui/ (corpus app).
Bottom line for the brainstorm: the engine is built, disciplined, and it surfaced the right thesis on real history. The honest gap is signal quality, and the highest-leverage fix is almost certainly corpus breadth + a finer cluster taxonomy (Grant's instinct), which would also let us resolve the cross-cluster gating debate from a position of strength rather than scarcity.
Note on dates: the quarterly as-of march is 2023-03, -05, -08, -11, 2024-02, -05, -08. The 2023-12 and 2024-03 columns are two ad-hoc single-date smoke runs (off the quarterly grid) that happen to be stored in the same table — included for completeness. The score for the SAME node at adjacent dates (e.g. 2023-11 vs 2023-12) swinging from 3.3 to 0 is itself a vivid illustration of the cadence-sensitivity problem.
Appendix A — Full score trajectories (the noise, concretely)
Every under-acted-conviction node × every as-of date that was scored. ★ = cleared the evidence bar. The point of showing this: watch the score and the acceleration a swing between adjacent quarters — that is the noise the write-up (§6.1) describes.
| derivative | 2023-03 | 2023-05 | 2023-08 | 2023-11 | 2023-12 | 2024-02 | 2024-03 | 2024-05 | 2024-08 |
|---|---|---|---|---|---|---|---|---|---|
| K2023 | 0.0 | 2.4★ | 0.0 | 0.0 | 1.6 | 0.8 | 0.8 | 0.0 | 0.0 |
| K2023-cooling | 0.8 | 0.0 | 0.0 | 0.0 | 0.0 | 1.6 | 1.6 | 0.0 | 0.0 |
| K2023-gas-turbines | 0.0 | 0.0 | 0.0 | 0.8 | 0.8 | 0.0 | 0.0 | 0.0 | 0.0 |
| K2023-grid-interconnect | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| K2023-nuclear | 0.0 | 2.0 | 0.0 | 0.0 | 0.0 | 0.8 | 0.0 | 0.0 | 0.0 |
| K2023-picks-and-shovels | 0.0 | 0.0 | 0.0 | 3.3 | 0.0 | 0.0 | 0.0 | 2.6★ | 0.0 |
| K2023-transformers | 0.0 | 0.0 | 0.8 | 0.5 | 0.0 | 0.0 | 0.0 | 4.8★ | 0.0 |
| K2023-uranium | 0.0 | 0.0 | 7.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 | 0.0 |
| K2023-utilities-repriced | 0.8 | 0.0 | 0.8 | 0.0 | 0.8 | 0.0 | 1.6 | 0.0 | 0.0 |
Detail — the acceleration sign-flips (why it's noisy)
For the headline derivative and the root, the raw inputs at each as-of (conf=confirmed corroborating claims, src=distinct sources, eisc=independence-weighted count, a=acceleration/2nd-derivative, k_eff=distinct independent clusters):
K2023
| as_of | score | cleared | conf | src | eisc | a | k_eff |
|---|---|---|---|---|---|---|---|
| 2023-03-01 | 0.00 | — | 0 | 0 | 0.0 | 0.0 | 0 |
| 2023-05-30 | 2.40 | YES | 6 | 4 | 3.0 | 1.0 | 2 |
| 2023-08-28 | 0.00 | — | 6 | 4 | 0.0 | -5.0 | 0 |
| 2023-11-26 | 0.00 | — | 6 | 4 | 0.0 | 3.0 | 0 |
| 2023-12-01 | 1.60 | — | 6 | 1 | 1.0 | 2.0 | 1 |
| 2024-02-24 | 0.80 | — | 7 | 4 | 1.0 | 1.0 | 1 |
| 2024-03-01 | 0.80 | — | 6 | 4 | 1.0 | 1.0 | 1 |
| 2024-05-24 | 0.00 | — | 9 | 6 | 1.6 | -0.4 | 1 |
| 2024-08-22 | 0.00 | — | 10 | 7 | 1.0 | -1.2 | 1 |
K2023-picks-and-shovels
| as_of | score | cleared | conf | src | eisc | a | k_eff |
|---|---|---|---|---|---|---|---|
| 2023-03-01 | 0.00 | — | 0 | 0 | 0.0 | 0.0 | 0 |
| 2023-05-30 | 0.00 | — | 2 | 2 | 1.0 | -1.0 | 1 |
| 2023-08-28 | 0.00 | — | 2 | 2 | 0.0 | -1.0 | 0 |
| 2023-11-26 | 3.33 | — | 4 | 3 | 1.6 | 2.6 | 1 |
| 2023-12-01 | 0.00 | — | 0 | 0 | 0.0 | 0.0 | 0 |
| 2024-02-24 | 0.00 | — | 5 | 3 | 1.0 | -2.2 | 1 |
| 2024-03-01 | 0.00 | — | 0 | 0 | 0.0 | 0.0 | 0 |
| 2024-05-24 | 2.56 | YES | 10 | 5 | 2.0 | 1.6 | 1 |
| 2024-08-22 | 0.00 | — | 5 | 3 | 0.0 | -1.0 | 0 |
K2023-utilities-repriced
| as_of | score | cleared | conf | src | eisc | a | k_eff |
|---|---|---|---|---|---|---|---|
| 2023-03-01 | 0.80 | — | 1 | 1 | 1.0 | 1.0 | 1 |
| 2023-05-30 | 0.00 | — | 0 | 0 | 0.0 | 0.0 | 0 |
| 2023-08-28 | 0.80 | — | 1 | 1 | 1.0 | 1.0 | 1 |
| 2023-11-26 | 0.00 | — | 3 | 2 | 1.0 | -1.0 | 1 |
| 2023-12-01 | 0.77 | — | 4 | 2 | 1.6 | 0.6 | 1 |
| 2024-02-24 | 0.00 | — | 4 | 3 | 1.0 | 0.0 | 1 |
| 2024-03-01 | 1.60 | — | 7 | 4 | 2.0 | 1.0 | 1 |
| 2024-05-24 | 0.00 | — | 0 | 0 | 0.0 | 0.0 | 0 |
| 2024-08-22 | 0.00 | — | 16 | 7 | 2.286 | -1.714 | 1 |
K2023-nuclear
| as_of | score | cleared | conf | src | eisc | a | k_eff |
|---|---|---|---|---|---|---|---|
| 2023-03-01 | 0.00 | — | 6 | 4 | 1.0 | 0.0 | 1 |
| 2023-05-30 | 2.05 | — | 5 | 3 | 1.6 | 1.6 | 1 |
| 2023-08-28 | 0.00 | — | 10 | 7 | 1.0 | -7.0 | 1 |
| 2023-11-26 | 0.00 | — | 0 | 0 | 0.0 | 0.0 | 0 |
| 2023-12-01 | 0.00 | — | 0 | 0 | 0.0 | 0.0 | 0 |
| 2024-02-24 | 0.80 | — | 6 | 4 | 1.0 | 1.0 | 1 |
| 2024-03-01 | 0.00 | — | 2 | 2 | 0.0 | 0.0 | 0 |
| 2024-05-24 | 0.00 | — | 0 | 0 | 0.0 | 0.0 | 0 |
| 2024-08-22 | 0.00 | — | 12 | 4 | 1.0 | -2.0 | 1 |
K2023-transformers
| as_of | score | cleared | conf | src | eisc | a | k_eff |
|---|---|---|---|---|---|---|---|
| 2023-03-01 | 0.00 | — | 0 | 0 | 0.0 | 0.0 | 0 |
| 2023-05-30 | 0.00 | — | 0 | 0 | 0.0 | 0.0 | 0 |
| 2023-08-28 | 0.80 | — | 1 | 1 | 1.0 | 1.0 | 1 |
| 2023-11-26 | 0.48 | — | 4 | 2 | 1.0 | 0.6 | 1 |
| 2023-12-01 | 0.00 | — | 0 | 0 | 0.0 | 0.0 | 0 |
| 2024-02-24 | 0.00 | — | 4 | 2 | 0.0 | -1.0 | 0 |
| 2024-03-01 | 0.00 | — | 6 | 4 | 0.0 | -1.0 | 0 |
| 2024-05-24 | 4.80 | YES | 8 | 5 | 2.0 | 3.0 | 1 |
| 2024-08-22 | 0.00 | — | 8 | 5 | 1.6 | -1.6 | 1 |