Product identity + widen books for DISPLAY, model input byte-identical

IDENTITY (CLAUDE.md top + MASTER-PLAN header). VYNDR is a PREDICTIVE MODEL:
it projects what a player will DO and picks accurately. Market edge is a
BYPRODUCT of a good prediction, never the success criterion. Success =
the forecast is honest about its own confidence AND still ranks --
calibration and resolution, both. No edge/CLV term belongs in a pass/fail
gate; they are diagnostics we report, not thresholds a model must clear.
A model tuned to beat a closing line has been fitted to the market instead
of to the game.

Per-sport doctrine (Phillips 2022, classify by what players DO not by
position): each sport is its own model -- own variables, archetypes,
conditions, calibration, honest ceiling. Shared across sports: ONLY the
Bayesian inference math.

Truth Law: no fabricated data; honest-absent over invented; label
limitations in-band; provisional stays provisional until re-run;
documented is not verified.

PHASE 2 -- AGGREGATOR WIDENING (live). normalizeProps now emits every
DISPLAY book instead of 5 of 18. Before this we discarded 13 books of our
own accord and 64.8% of the MLB slate was invisible to users. Every prop
carries book_role (both/takeable/reference/dfs/offshore) so the display
layer can say WHAT a price is -- a fixed-payout DFS number and a two-way
sportsbook price are not interchangeable objects. Unknown books are still
dropped.

PHASE 3 -- MODEL GATE (the model does not move). bookRoles splits
MODEL_BOOKS (the legacy allow-list, character for character) from
DISPLAY_BOOKS. Both model paths re-filter before they pick a line:
gradeSlateService.dedupeProps (before first-row-wins AND before the limit)
and intradayRefreshService.indexOddsProps (which RE-GRADES at the current
line -- without the gate, widening would have silently moved locked lines
onto books the model has never been calibrated against). A test asserts
the graded set is byte-identical through the widening.

CURRENT_RULER_VERSION stays v1_first_book. The gate lifts only when the
MLB calibration is re-run on the consensus ruler and v2 is promoted.

HONEST FRAMING, recorded in the plan: this is an AGGREGATOR win and it
does NOT fix the model. WNBA still abstains -- a model problem, not a
coverage problem; it is better covered than MLB. MLB isotonic still
provisional. The consensus is MARKET, not SHARP: pinnacle, matchbook and
polymarket are 0% on both sports, so no sharp anchor exists in our feed.

Two superseded tests updated to stronger properties rather than deleted:
roleOf now names the KIND of book, and the normalizer test asserts the
display set widens WHILE the model set does not.

Gates: 4,027 tests / 322 suites green; next build exit 0.

Co-Authored-By: Claude Opus 5 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01QJs13VsyiSKYQP6rj3NNmc

src/services/gradeSlateService.js

@@ -26,16 +26,28 @@
 // We dedupe to unique player+stat+line and cap how many we grade, because
 // each grade fans out to feature computation. Grading runs at most once per
 // cache-miss per sport, but we still bound the herd.
+const { isModelBook } = require('../config/bookRoles');
+
 const DEFAULT_LIMIT = 25;
 const DEFAULT_CONCURRENCY = 5;
 const DEFAULT_TTL = 7200; // 2 hours — matches the spec's grades-cache TTL.
 
 // Collapse the multi-book prop rows to one entry per gradeable prop.
+//
+// ORDER ZERO GATE (2026-08-01). `normalizeProps` now emits every DISPLAY book so
+// the surfaces can shop lines, which means DFS pick'em and exchange rows arrive
+// here for the first time. The MODEL must not eat them: it has never been
+// measured against those books, and a fixed-payout DFS number is not a market
+// price at all. So we re-filter to MODEL_BOOKS BEFORE the first-row-wins pick
+// and before the limit — which makes the graded set byte-identical to what it
+// was before the widening. This gate lifts only when the MLB calibration is
+// re-run on the consensus ruler and v2 is promoted.
 function dedupeProps(props, limit) {
   const seen = new Set();
   const out = [];
   for (const p of props || []) {
     if (!p || !p.player || !p.stat_type || p.line == null) continue;
+    if (!isModelBook(p.book)) continue;
     const key = `${p.player}::${p.stat_type}::${p.line}`;
     if (seen.has(key)) continue;
     seen.add(key);