fix(eval): correct probe metric to use true year-based OOS split

- train.py build(): year-based split (train≤2021, OOS≥2022) replaces
  misleading 70/30 mixed-period split; true OOS val_vol_r2 now ~-0.36
  vs previously reported +0.18 (artefact of cross-period data leakage)
- train.py: EXPORT_EMBEDDINGS block now exports both train+OOS embeddings
  with dates and HV labels for Go eval harness
- cmd/eval: LinearProbeTrainTest uses train stats for standardisation of
  both sets (no leakage); standardiseCompute/applyStandardise helpers
- internal/eval: add LinearProbeTrainTest (fit-on-train, eval-on-OOS)
  alongside LinearProbe (same-set); 8/8 tests still green

Phase-0 gate result: val_vol_r2=-0.36, silhouette=0.043, erank=58.9/64.
Backbone produces high-rank embeddings (SIGReg working) but does NOT
generalize across 2021→2022 regime boundary. Gate: INCONCLUSIVE/FAIL.

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

internal/eval/eval.go

@@ -10,6 +10,58 @@ import (
 	"math"
 )
 
+// LinearProbeTrainTest fits ridge regression on (trainEmb, trainY) and evaluates
+// on (testEmb, testY). Returns OOS R². Use this for proper held-out evaluation.
+func LinearProbeTrainTest(trainEmb [][]float64, trainY []float64,
+	testEmb [][]float64, testY []float64, lambda float64) float64 {
+	n := len(trainEmb)
+	if n == 0 || len(testEmb) == 0 {
+		return 0
+	}
+	d := len(trainEmb[0])
+	p := d + 1
+
+	A := make([][]float64, n)
+	for i, e := range trainEmb {
+		row := make([]float64, p)
+		copy(row, e)
+		row[d] = 1.0
+		A[i] = row
+	}
+	AtA := make([][]float64, p)
+	for i := range AtA {
+		AtA[i] = make([]float64, p)
+	}
+	Aty := make([]float64, p)
+	for i := 0; i < n; i++ {
+		for j := 0; j < p; j++ {
+			Aty[j] += A[i][j] * trainY[i]
+			for k := 0; k < p; k++ {
+				AtA[j][k] += A[i][j] * A[i][k]
+			}
+		}
+	}
+	for j := 0; j < p; j++ {
+		AtA[j][j] += lambda
+	}
+	w := solveCholesky(AtA, Aty)
+
+	yMean := mean(testY)
+	var ssRes, ssTot float64
+	for i, e := range testEmb {
+		row := make([]float64, p)
+		copy(row, e)
+		row[d] = 1.0
+		pred := dot(row, w)
+		ssRes += (testY[i] - pred) * (testY[i] - pred)
+		ssTot += (testY[i] - yMean) * (testY[i] - yMean)
+	}
+	if ssTot == 0 {
+		return 0
+	}
+	return 1 - ssRes/ssTot
+}
+
 // LinearProbe fits a ridge regression (closed-form) on (emb, y) with regularisation λ
 // and returns R² on the same data. Call with train embeddings; probe on held-out by
 // splitting before calling.