feat(WP-0013): IHF Phase 12 — Platform Memory and Continuous Learning

Closes the long-range feedback loop: outcome signals now enrich the full
traceability chain and feed back into routing, triage, and AI proposals.

Schema (T01):
- outcome_correlations (CHECK correlation_type)
- pattern_performance_records
- adaptive_threshold_configs
- institutional_knowledge_entries (GIN tsvector FTS)
- learning_insights (CHECK insight_type)
- ALTER TABLE decision_records + requirement_candidates: outcome_summary JSONB
- AFTER INSERT trigger trg_enrich_lineage on outcome_signals
- contracts/core/ updated (outcome-summary-columns-v1, append-only addendum)

Correlation engine (T02):
- Application/Helper/CorrelationEngine.hs: pure annotation→outcome SQL
- Web/Controller/OutcomeCorrelations.hs: ComputeCorrelationsAction + index

Pattern performance (T03):
- Web/Controller/PatternPerformance.hs: ComputePatternPerformanceAction

Adaptive thresholds (T04):
- Web/Controller/AdaptiveThresholds.hs: CalibrateThresholdsAction
- Application/Helper/FrictionScore.hs: applyAdaptiveWeights

Institutional knowledge (T05):
- DistilDecisionAction in DecisionRecords controller
- Web/Controller/InstitutionalKnowledge.hs: QueryKnowledgeBaseAction

Lineage enrichment (T06):
- Web/Controller/LineageEnrichment.hs: EnrichLineageAction (batch backfill)
- enrich_lineage_on_outcome_batch() PL/pgSQL helper in migration

Learning dashboard (T07):
- Web/Controller/LearningDashboard.hs: 5-panel autoRefresh view
- "Learning" nav link in FrontController

API v2 learning endpoints (T08):
- GET /api/v2/outcome-correlations, /pattern-performance, /knowledge-base/{id}
- OpenAPI schemas: OutcomeCorrelation, PatternPerformanceRecord, InstitutionalKnowledgeEntry

GAAF scorecard + docs (T09):
- Core 3.8→3.9, Functional 3.6→3.8, overall 3.61→3.68
- CLAUDE.md: IHF v0.2 complete, no active workplan

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

Web/Controller/AdaptiveThresholds.hs

@@ -0,0 +1,86 @@
+module Web.Controller.AdaptiveThresholds where
+
+-- IHF Phase 12 — Platform Memory (IHUB-WP-0013 T04)
+
+import Web.Controller.Prelude
+import Web.View.AdaptiveThresholds.Index
+import IHP.ModelSupport (sqlQuery)
+import Database.PostgreSQL.Simple (Only(..))
+
+instance Controller AdaptiveThresholdsController where
+    beforeAction = ensureIsUser
+
+    action AdaptiveThresholdsAction = do
+        hubs    <- query @Hub |> orderByAsc #name |> fetch
+        configs <- query @AdaptiveThresholdConfig |> fetch
+        insights <- query @LearningInsight
+            |> filterWhere (#insightType, "threshold_calibration")
+            |> orderByDesc #computedAt
+            |> limit 10
+            |> fetch
+        render IndexView { hubs, configs, insights }
+
+    action CalibrateThresholdsAction { hubIdForThreshold } = do
+        let hubId = hubIdForThreshold
+        -- Step 1: find weak-predictor categories (score < 0.3)
+        weakCats <- sqlQuery
+            "SELECT annotation_category FROM outcome_correlations \
+            \ WHERE hub_id = ? AND correlation_score < 0.3"
+            [hubId]
+            :: IO [Only Text]
+
+        -- Step 2: compute bottleneck threshold override = mean friction score
+        -- for widgets with at least one negative outcome signal
+        [Only mBottleneckOverride] <- sqlQuery
+            "SELECT AVG(fs.score) \
+            \ FROM friction_scores fs \
+            \ JOIN widgets w ON w.id = fs.widget_id \
+            \ WHERE w.hub_id = ? \
+            \   AND EXISTS ( \
+            \       SELECT 1 FROM outcome_signals os \
+            \       JOIN deployment_records dep ON dep.id = os.deployment_id \
+            \       JOIN decision_records dr ON dr.id = dep.decision_id \
+            \       JOIN requirements r ON r.id = dr.requirement_id \
+            \       JOIN requirement_candidates rc ON rc.id = r.candidate_id \
+            \       WHERE rc.source_widget_id = w.id \
+            \         AND os.signal_type NOT IN ('success','adoption','satisfaction') \
+            \   )"
+            [hubId]
+            :: IO [Only (Maybe Double)]
+
+        now <- getCurrentTime
+        let weakNote = "Weak predictor categories (score < 0.3): "
+                    <> intercalate ", " (map fromOnly weakCats)
+
+        -- Step 3: upsert AdaptiveThresholdConfig
+        existing <- query @AdaptiveThresholdConfig
+            |> filterWhere (#hubId, hubId)
+            |> fetchOneOrNothing
+        case existing of
+            Just cfg ->
+                cfg
+                    |> set #bottleneckThresholdOverride mBottleneckOverride
+                    |> set #calibrationDate now
+                    |> set #notes (Just weakNote)
+                    |> updateRecord
+            Nothing ->
+                newRecord @AdaptiveThresholdConfig
+                    |> set #hubId hubId
+                    |> set #weightOverrides (A.Object mempty)
+                    |> set #bottleneckThresholdOverride mBottleneckOverride
+                    |> set #calibrationDate now
+                    |> set #notes (Just weakNote)
+                    |> createRecord
+
+        -- Step 4: write LearningInsight
+        newRecord @LearningInsight
+            |> set #hubId hubId
+            |> set #insightType "threshold_calibration"
+            |> set #title "Adaptive threshold calibration completed"
+            |> set #body ("Calibrated friction thresholds. " <> weakNote
+                <> maybe "" (\b -> " Bottleneck override: " <> show b) mBottleneckOverride)
+            |> set #evidenceLinks (A.toJSON ([] :: [A.Value]))
+            |> createRecord
+
+        setSuccessMessage "Threshold calibration complete"
+        redirectTo AdaptiveThresholdsAction