feat(gun_harness): runtime metric switch + A/B proving the point metric mis-selects

Adds GUN_VBULLET_METRIC (point|path, default point = unchanged behaviour) so
the virtual-bullet hit model can be selected at runtime with no rebuild. Both
the live tracker and the offline replay read the same value, so the 12/12
offline==online acceptance holds under EITHER setting (verified for both).

A/B AGAINST THE LIVE BOSS, real server-side hit rate as ground truth, 5
battles x 12 rounds per metric on one frozen binary:
  point  4660 shots / 219 hits = 4.70%   (per-run 3.16-5.53)
  path   4834 shots / 359 hits = 7.43%   (per-run 6.55-8.24)
The distributions DO NOT OVERLAP: path's worst run beats point's best run.
+2.73pp, +58% relative, z = 5.56, p < 0.0001. Range distributions were
identical (~460-478 px), so this is not a range confound.

MECHANISM - and this is the important part. The gain is SELECTION, not better
gun learning. Under the point model every gun's virtual rate is compressed
into 0.6-4.4%, so HeadOn sits inside the 2pp tie margin and takes 72.6% of
selection ticks / 76.9% of shots - while HeadOn is 11th of 13 by REAL hit rate
(2.3%). The path model widens the band to 4.7-13.7% and ranks HeadOn 10th, so
its shot share falls to 35.9% and Pattern/Accel/WallBounce get picked instead.
Counterfactual: applying the point model's per-gun real rates to the path
model's shot mix yields 7.65%, i.e. essentially the whole observed gain.
So the selector, not the guns, is where the win lives.

PER-GUN REAL HIT RATE vs DrussGT (path mix, the answer to 'which guns are
worth keeping'): WallBounce 10.8, Pattern 10.5, Accel 10.0, Displace 9.3,
Circular 9.2, AvgLead 8.5, KNN 5.7, StopShot 5.2, GuessFactor 3.7,
Tsetlin 2.9. Per-gun N is small (hundreds of shots) so single-gun ordering is
indicative, not definitive.

TWO CAVEATS, recorded because they undercut a naive reading:
1. One adversary. DrussGT is a wave surfer and HeadOn is genuinely bad against
   surfers, so part of this may be matchup-specific.
2. The path model is NOT a better general ranker. Spearman(virtual rank, real
   rank) is 0.52 under point vs -0.04 under path. It wins by accidentally
   fixing HeadOn's mis-rank, not by ranking guns better. A more durable fix is
   to address the selection logic directly - which is the next job.

Also adds a focused guard test (test_vbullet_metric) covering parsing/default,
a receding-target point-miss/path-hit, a perpendicular-target path-miss, and
replay determinism.

Verified: 33 guard checks, 12/12 acceptance under both metrics, tsetlin tests
green, range 34.3% (point, unchanged) / 50.8% (path).

common_libs/gun_harness/offline_range.nim

@@ -218,7 +218,8 @@ proc reportFor(tracker: VirtualTracker, drivers: seq[GunDriver],
     result.add r
 
 proc replayFixture*(fx: Fixture, drivers: seq[GunDriver],
-                    targetId = -1, liveActual = false): seq[GunReport] =
+                    targetId = -1, liveActual = false,
+                    metric = ActiveMetric): seq[GunReport] =
   ## Drive a fresh `VirtualTracker` over the whole fixture, one tick at a time,
   ## in the same order the live loop uses:
   ##   1. predict(state, bulletSpeed(PowerBins[i])) for i = 0..3, per gun
@@ -238,9 +239,12 @@ proc replayFixture*(fx: Fixture, drivers: seq[GunDriver],
   ##     resolution) is skipped entirely; the end marker tells us so and we drop
   ##     the final tick's resolutions. Synthetic fixtures leave liveActual false
   ##     (the state at the resolution tick is the ground truth).
+  ##
+  ## `metric` defaults to the process-wide `GUN_VBULLET_METRIC` switch read by
+  ## virtual_bullets; pass it explicitly only to force a model in one process.
   let tid = if targetId >= 0: targetId else: fx.enemyId
   let skipFinal = liveActual and fx.enemyDied
-  var tracker = initTracker(drivers.len)
+  var tracker = initTracker(drivers.len, metric)
   for si in 0..<fx.states.len:
     let state = fx.states[si]
     for gi in 0..<drivers.len: