Take model tuning from config too, not from the saved state

The companion to the Kalman fix. RecursiveLeastSquares.from_dict and
AdaptiveConformal.from_dict restore lambda, delta, alpha, gamma and the
conformal window alongside their data, and load_dict replaces the
config-built heads with those, so every one of those knobs was immutable on
any station that already had state. Editing config.yaml and restarting looks
exactly like a change with no effect, which is the failure mode that cost real
time on the Kalman side of this before it was found.

Only the estimate is state now. Weights, covariances and conformal scores are
restored; tuning is re-applied from config on every load. The conformal deques
are rebuilt when the configured window changes, preserving their contents.

load_dict also skips heads for a target or horizon this build no longer has,
rather than resurrecting them from a stale file.

Found while implementing a damped-trend ensemble member, which was then
abandoned: see the following note.

ashvale/models/rls.py

@@ -183,6 +183,20 @@ class AdaptiveConformal:
         self.alpha = float(np.clip(self.alpha + self.gamma * (self.alpha_target - err),
                                    0.005, 0.75))
 
+    def retune(self, alpha: float, gamma: float, window: int) -> None:
+        """Re-apply configured tuning, keeping the observed scores.
+
+        from_dict restores alpha_target, gamma and the window alongside the
+        data, so editing any of them in config.yaml did nothing on a station
+        that already had state: the file put the old values straight back.
+        """
+        self.alpha_target = float(alpha)
+        self.gamma = float(gamma)
+        window = int(window)
+        if self.scores.maxlen != window:
+            self.scores = deque(self.scores, maxlen=window)
+            self.hits = deque(self.hits, maxlen=window)
+
     @property
     def empirical_coverage(self) -> float:
         return float(np.mean(self.hits)) if self.hits else float("nan")