Source identity
| Field | Recorded value |
|---|---|
| Source type | GitHub source |
| Original record | Janady13/missionmatch / db/migrations/0041_capture_readiness.sql |
| Date | Not specified in source |
| Repository | Janady13/missionmatch |
| Revision | 720d47fae476b0d38477ae4401697d372b1ea2bc |
| Branch | feature/opportunity-to-outcome |
| Path or archive member | db/migrations/0041_capture_readiness.sql |
| Original SHA-256 | 2188b162ce3d852434326f9dab0db8629627dd2ab6e1c6314344e8c415693046 |
| Git blob SHA | a6e28cf9e33f303b8af543fc6be6e87ce8edb6a4 |
| Reading-copy SHA-256 | 2188b162ce3d852434326f9dab0db8629627dd2ab6e1c6314344e8c415693046 |
Every recorded occurrence
These 6 records contain identical original source bytes. Their paths and revisions remain part of the archive.
| Original record | Branch or archive | Revision / identifier | Original location |
|---|---|---|---|
| Janady13/missionmatch / db/migrations/0041_capture_readiness.sql | feature/opportunity-to-outcome | 720d47fae476b0d38477ae4401697d372b1ea2bc | Open original |
| Janady13/missionmatch / db/migrations/0041_capture_readiness.sql | master | 1e9249840a4302ec82e123b9c9cb6f75badea3e4 | Open original |
| Janady13/missionmatch / db/migrations/0041_capture_readiness.sql | presentation-readiness | eb574acaa29d71ce4d0f852572e87c275c79fdb7 | Open original |
| Janady13/missionmatch / db/migrations/0041_capture_readiness.sql | vendor-intelligence | fe9796a19c261951d3f2565206c0c1b5ac294465 | Open original |
| Janady13/missionmatch / db/migrations/0041_capture_readiness.sql | feature/aioe-upgrade | 2178e7298d0e5d9af83afa12402fd587f0ba6ed8 | Open original |
| Janady13/missionmatch / db/migrations/0041_capture_readiness.sql | capture-readiness | bae6af7cb2997e819174a9e9cfff5e16067a1975 | Open original |
Recovered source transcript
Download complete source text ↓37 lines · 3.0 KB
-- +goose Up
-- Seed the tunable weights of the Capture Readiness bid pursue scorecard (Phase 1 of the Mission Workforce
-- Technologies module set). It rides the SAME coefficient store as the learned models (no new tables), but it
-- is a DETERMINISTIC WEIGHTED COMPOSITION, not a logistic model: the serve path (answerCaptureReadiness) reads
-- these coefficients as the FACTOR WEIGHTS and computes an EXACT decimal weighted average of the four factors
-- (each already in [0, 1]), never a logistic sigmoid, then maps the score to a GO / REVIEW / NO_GO recommendation.
-- Storing the weights here (not in code) lets a governance owner tune the capture logic without a code change,
-- and a later phase can fit a learned calibration on realized bid_outcomes on the same surface. The OutputKind
-- is opportunity_score: it is an OPPORTUNITY level bid decision aid, never a per candidate score, so the frozen
-- v1.4.0 engine stays the only per candidate scorer. A weighted average needs NO calibration bins, so none are
-- seeded. The weights are the governance chosen defaults: staffing readiness 0.35, market win likelihood 0.30,
-- incumbency position 0.20, past performance 0.15 (they sum to one).
INSERT INTO aedt_learned_models (model_key, version, family, output_kind, feature_spec, champion, notes) VALUES (
'opportunity_capture_readiness',
1,
'weighted_scorecard',
'opportunity_score',
'{"factors": [{"name": "staffing_readiness", "source": "opportunity_requirements_green_coverage"}, {"name": "market_win_likelihood", "source": "bid_win_rate_by_segment"}, {"name": "incumbency_position", "source": "agency_award_concentration"}, {"name": "past_performance", "source": "segment_engagement_depth"}]}',
true,
'Deterministic Capture Readiness scorecard v1. The coefficients are FACTOR WEIGHTS (staffing 0.35, market 0.30, incumbency 0.20, past performance 0.15) composed as an exact decimal weighted average, never a logistic sigmoid. GO at score >= 0.65, REVIEW 0.45 to 0.65, NO_GO below 0.45. OutputKind opportunity_score, an opportunity level bid pursue advisory, never a per candidate score.'
) ON CONFLICT (model_key, version) DO NOTHING;
-- The four factor weights plus the reserved intercept (0.0; a weighted average has no bias term, stored only so
-- the shape matches the other models).
INSERT INTO aedt_model_coefficients (model_key, version, feature_name, coefficient) VALUES
('opportunity_capture_readiness', 1, 'intercept', 0.0000000000),
('opportunity_capture_readiness', 1, 'staffing_readiness', 0.3500000000),
('opportunity_capture_readiness', 1, 'market_win_likelihood', 0.3000000000),
('opportunity_capture_readiness', 1, 'incumbency_position', 0.2000000000),
('opportunity_capture_readiness', 1, 'past_performance', 0.1500000000)
ON CONFLICT (model_key, version, feature_name) DO NOTHING;
-- +goose Down
DELETE FROM aedt_model_coefficients WHERE model_key = 'opportunity_capture_readiness';
DELETE FROM aedt_learned_models WHERE model_key = 'opportunity_capture_readiness';