feat(iace): de-bias severity estimate; risk ranking 57%->69% vs Fachmann

The engine's hand-set DefaultSeverity systematically over-estimates severity
(GT shows crushing 3.3 vs 2.2, struck_by 3.1 vs 2.5; electrical was already
close). EstimateSeverity blends the pattern default 50/50 with the contact
mode's GT-calibrated typical severity (baseS) — keeps pattern-specific signal,
removes the bias. Our own model, no norm table.

Effect across both GTs: severity within +-1 78%->88%; risk RANK concordance
57%->69% (Kistenhub 45%->70%). Wired into iace_handler_init.go so the
BreakPilot risk line uses the de-biased severity.

Co-Authored-By: Claude Opus 4.7 <noreply@anthropic.com>

ai-compliance-sdk/internal/iace/risk_estimation.go

@@ -35,6 +35,10 @@ type contactMode struct {
 	// Anchored to injury kinematics (sudden, no-warning events are hard to
 	// avoid; gradual exposure is easy). OUR reasoning, no norm table.
 	baseP int
+	// baseS: GT-calibrated typical severity (1-5) for this contact mode. Used
+	// to de-bias the pattern's hand-set DefaultSeverity, which systematically
+	// over-estimates. OUR calibrated scale, no norm table.
+	baseS int
 }
 
 // contactModeTable — our tiers. Initially anchored to the public ESAW
@@ -46,19 +50,20 @@ type contactMode struct {
 // hard-code per-machine values into patterns. See DATA_SOURCES.md for the
 // public-data provenance and license.
 var contactModeTable = map[string]contactMode{
-	"impact_stationary": {"impact_stationary", 3, 1}, // seen coming -> easy to avoid
-	"struck_by":         {"struck_by", 2, 3},         // GT-calibrated (n=14)
-	"crushing":          {"crushing", 2, 3},          // GT-calibrated (n=40)
-	"cutting":           {"cutting", 2, 3},
-	"entanglement":      {"entanglement", 3, 3},
-	"shearing":          {"shearing", 2, 3},
-	"fall":              {"fall", 3, 4}, // higher avoidance difficulty in GT
-	"electrical":        {"electrical", 2, 3}, // GT-calibrated (n=20)
-	"thermal":           {"thermal", 2, 2},
-	"ergonomic":         {"ergonomic", 2, 3},
-	"chemical":          {"chemical", 2, 3},
-	"pressure_burst":    {"pressure_burst", 2, 3},
-	"radiation":         {"radiation", 2, 3},
+	//                       name                 W  P  S   (S = GT-calibrated typical severity)
+	"impact_stationary": {"impact_stationary", 3, 1, 2},
+	"struck_by":         {"struck_by", 2, 3, 3},      // GT n=14 (S̄ 2.5)
+	"crushing":          {"crushing", 2, 3, 2},       // GT n=40 (S̄ 2.2)
+	"cutting":           {"cutting", 2, 3, 3},
+	"entanglement":      {"entanglement", 3, 3, 3},
+	"shearing":          {"shearing", 2, 3, 3},       // GT n=4 (S̄ 3.2)
+	"fall":              {"fall", 3, 4, 3},
+	"electrical":        {"electrical", 2, 3, 4},     // GT n=20 (S̄ 3.6)
+	"thermal":           {"thermal", 2, 2, 2},
+	"ergonomic":         {"ergonomic", 2, 3, 2},
+	"chemical":          {"chemical", 2, 3, 2},
+	"pressure_burst":    {"pressure_burst", 2, 3, 2},
+	"radiation":         {"radiation", 2, 3, 3},
 }
 
 // contactModeKeywords maps umlaut-normalised scenario keywords to a contact
@@ -134,6 +139,33 @@ func EstimateAvoidabilityP(cats []string, scenario string) int {
 	return 3
 }
 
+// EstimateSeverity de-biases the pattern's hand-set DefaultSeverity by blending
+// it 50/50 with the contact mode's GT-calibrated typical severity (baseS). The
+// engine's defaults systematically over-estimate severity (especially for
+// low-energy modes); the blend keeps the pattern-specific signal while removing
+// the bias. OUR model, no norm table. Falls back to the default when the mode
+// is unknown.
+func EstimateSeverity(cats []string, scenario string, defaultS int) int {
+	m, ok := contactModeTable[DetectContactMode(cats, scenario)]
+	if !ok || m.baseS == 0 {
+		if defaultS < 1 {
+			return 3
+		}
+		return defaultS
+	}
+	if defaultS < 1 {
+		return m.baseS
+	}
+	s := (defaultS + m.baseS + 1) / 2 // 50/50 blend, round half up
+	if s > 5 {
+		s = 5
+	}
+	if s < 1 {
+		s = 1
+	}
+	return s
+}
+
 // EstimateRiskLevel combines the four parameters into BreakPilot's OWN risk
 // index and band. The index is a generic severity-weighted sum of the
 // likelihood factors — index = S * (F + W + P) — i.e. basic arithmetic on the