diff --git a/Experiments/v7.py b/Experiments/v7.py
index f25c10f..f39962c 100644
--- a/Experiments/v7.py
+++ b/Experiments/v7.py
@@ -443,7 +443,7 @@ def evaluation_metrics(deltas, sigma, tau, max_lag=MAX_LAG):
     cov   = compute_coverage(tau, sigma, max_lag)
     return dict(
         mean_delta = float(np.mean(deltas)),
-        pct_early  = float(np.mean(valid)),        # Precision
+        precision  = float(np.mean(valid)),        # Precision
         recall     = float(cov),                   # Coverage = Recall
         mean_early = float(np.mean(deltas[valid])) if valid.any() else 0.0,
         std_delta  = float(np.std(deltas)),
@@ -503,7 +503,7 @@ def threshold_sweep(model, X_scaled, X_raw, sigma,
         rows.append({
             'pct'       : pct,
             'mean_delta': m['mean_delta'],
-            'precision' : m['pct_early'],
+            'precision' : m['precision'],
             'recall'    : m['coverage'],
             'n_tau'     : m['n_tau'],
             'n_early'   : m['n_early'],
@@ -584,7 +584,7 @@ def run_robustness_grid(model_base, scaler_base,
                 rows.append({'delay': dname, 'noise': nname, 'strength': sname,
                              'rep': rep,
                              'mean_delta': m['mean_delta'],
-                             'precision' : m['pct_early'],
+                             'precision' : m['precision'],
                              'recall'    : m['coverage'],
                              'n_tau'     : m['n_tau'],
                              'n_sigma'   : len(sig_r)})
@@ -614,14 +614,14 @@ def channel_lead_time_analysis(comps, sigma, Z_true, max_lag=MAX_LAG,
         tau   = apply_early_detection_constraint(tau, sigma,
                                                   min_lead=min_lead, max_lag=max_lag)
         if len(tau) == 0:
-            results[key] = dict(mean_delta=np.nan, pct_early=0.0,
+            results[key] = dict(mean_delta=np.nan, precision=0.0,
                                  coverage=0.0, n_tau=0)
             continue
         deltas = compute_lead_times(tau, sigma, max_lag)
         m      = evaluation_metrics(deltas, sigma, tau, max_lag)
         results[key] = dict(
             mean_delta = m['mean_delta'],
-            pct_early  = m['pct_early'],
+            precision  = m['precision'],
             coverage   = m['coverage'],
             n_tau      = m['n_tau'],
         )
@@ -848,7 +848,7 @@ def plot_channel_diagnostics(comps, diag_results, winner_counts, lead_by_channel
                  fontsize=12, fontweight="bold")
 
     # Panel A: grouped bar
-    metrics = ['mean_delta', 'pct_early', 'coverage']
+    metrics = ['mean_delta', 'precision', 'coverage']
     m_labels = ['Mean Δ (scaled)', 'Precision', 'Coverage']
     x = np.arange(len(keys))
     width = 0.22
@@ -1091,7 +1091,7 @@ def run_experiment():
             "Detector"       : name,
             "Mean Δ"         : f"{m['mean_delta']:+.2f}",
             "95% CI"         : f"[{lo:+.2f}, {hi:+.2f}]",
-            "Precision"      : f"{m['pct_early']:.1%}",
+            "Precision"      : f"{m['precision']:.1%}",
             "Coverage"       : f"{m['coverage']:.1%}",
             "Mean Δ|early"   : f"{m['mean_early']:+.2f}",
             "N(τ)"           : m['n_tau'],
@@ -1118,7 +1118,7 @@ def run_experiment():
                                  pct_range=SWEEP_PCTS, method='adaptive')
     sweep_mtr = threshold_sweep(model, X_scaled, X_raw, sigma,
                                  pct_range=SWEEP_PCTS, method='multitrigger')
-    print(f"  Sweep complete. Standard: {sweep_std['pct_early'].notna().sum()} "
+    print(f"  Sweep complete. Standard: {sweep_std['precision'].notna().sum()} "
           f"valid thresholds / {len(SWEEP_PCTS)}")
 
     # ── 7. Signal diagnostics ────────────────────────────────────
@@ -1128,7 +1128,7 @@ def run_experiment():
     print("  Channel standalone performance:")
     for key, res in diag_results.items():
         print(f"    {key:15s}: mean Δ={res['mean_delta']:+.2f}  "
-              f"precision={res['pct_early']:.1%}  coverage={res['coverage']:.1%}  "
+              f"precision={res['precision']:.1%}  coverage={res['coverage']:.1%}  "
               f"n_tau={res['n_tau']}")
     print("  Earliest-trigger counts per channel:")
     for key, cnt in sorted(winner_counts.items(), key=lambda x: -x[1]):
@@ -1150,12 +1150,12 @@ def run_experiment():
     m_std = evaluation_metrics(delta_dict['Model'], sigma, tau_std)
     print(f"\n  ── FINAL SUMMARY ─────────────────────────────────────────")
     print(f"  Model  Mean Δ      : {m_std['mean_delta']:+.2f} steps")
-    print(f"  Model  Precision   : {m_std['pct_early']:.1%}")
+    print(f"  Model  Precision   : {m_std['precision']:.1%}")
     print(f"  Model  Coverage    : {m_std['coverage']:.1%}")
     print(f"  Model  Mean Δ|early: {m_std['mean_early']:+.2f} steps")
     checks = [
         (m_std['mean_delta'] > 0,     "Positive mean lead-time"),
-        (m_std['pct_early']  > 0.60,  "> 60% precision"),
+        (m_std['precision']  > 0.60,  "> 60% precision"),
         (m_std['coverage']   > 0.30,  "> 30% coverage"),
     ]
     for ok, msg in checks: