Keysat
50 lines
2.5 KiB
Python
50 lines
2.5 KiB
Python
"""The quantitative bar (§5.1, §6.6) — the single gate between nomination and the frontier judge.
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Two tiers:
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- evidence bar → clears hard gates → WRITE A LEDGER ROW (the denominator, §6.6), even if never judged.
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- promotion bar → also clears the score threshold → goes to the frontier judge.
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THE GLOBAL META-RULE (applied to every scorer): no candidate clears on a single source or single
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cluster — EISC_adj >= 2.0 AND K_eff >= 2. This is the §2.1 anti-lonely-outlier law, enforced once.
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"""
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from __future__ import annotations
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EISC_FLOOR = 2.0
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KEFF_FLOOR = 2
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# Defaults; overridable via the score_thresholds table (so the backtest can sweep without code edits).
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DEFAULT_MIN_SCORE = {"under_acted": 0.3, "emergence": 2.0, "contrarian": 1.5,
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"convergence": 2.5, "intersection": 2.0}
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def _min_score(conn, scorer: str) -> float:
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if conn is not None:
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row = conn.execute("SELECT min_score FROM score_thresholds WHERE scorer=?", (scorer,)).fetchone()
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if row and row[0] is not None:
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return float(row[0])
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return DEFAULT_MIN_SCORE.get(scorer, 0.0)
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def evaluate(scorer: str, result: dict, *, conn=None) -> tuple[bool, bool]:
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"""Returns (cleared_evidence_bar, cleared_promotion_bar)."""
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if scorer == "under_acted":
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return _under_acted(result, _min_score(conn, scorer))
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return (False, False) # Job A scorers wired with the forward pilot
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def _under_acted(result: dict, min_score: float) -> tuple[bool, bool]:
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i = result["inputs"]
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breaker = bool(i.get("is_breaker"))
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# §4.4 Job B = "rising INDEPENDENT corroboration". EISC>=2.0 enforces independence (shared-guest +
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# same-cluster discounting), so this is NOT an isolated point or one-guest echo (§2.1). Cross-cluster
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# (k_eff>=2) is the §4.5 GOLD for Job A DISCOVERY — NOT a hard gate for Job B corroboration: N
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# independent energy companies confirming a power thesis is real corroboration. Cross-cluster still
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# BOOSTS the score (eisc_corrob = eisc_adj includes the xcluster_mult) so cross-cluster ranks first.
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corroborated = (i.get("n_confirmed", 0) >= 4 and i.get("n_src", 0) >= 2
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and i.get("eisc_corrob", 0.0) >= EISC_FLOOR and i.get("a_corrob", 0.0) > 0)
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conv_ok = breaker or i.get("conviction_weight", 0.0) >= 0.7 # med-high / high
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expo_ok = breaker or i.get("exposure") in ("none", "lt2") # genuine exposure gap
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evidence = corroborated and conv_ok and expo_ok
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promotion = evidence and result["score"] >= min_score
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return evidence, promotion
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