feat: per-class max box fraction (REQ-188)

- labeling/preview/autolabel: max_box_frac + per-class max_box_fracs
  ceiling filter (0=none, 1=off) before NMS, mirrors min_box_frac
- exemplar review-assist: ceiling before max_detections truncation;
  ExemplarLabelRequest + filter panel 'Max box size' slider
- ClassParamsTable: MaxBox column; copy line now REQ-186 amended
  order: class <name> conf <v> iou <v> minbox <v> maxbox <v> container
- docs: design/ui-spec/tasks updated (incl. stale 4-slider narrative)

backend/exemplar.py

@@ -37,6 +37,7 @@ DEFAULTS = {
     "threshold": 0.5,
     "iou_threshold": 0.8,
     "min_box_frac": 0.002,
+    "max_box_frac": 1.0,
     "max_detections": 100,
 }
 
@@ -116,7 +117,8 @@ def _drop_negative_overlaps(frame_id: int, class_id: int,
 
 def label(frame_id: int, class_id: int, exemplars: List[dict],
           threshold: float = 0.5, iou_threshold: float = 0.8,
-          min_box_frac: float = 0.002, max_detections: int = 100,
+          min_box_frac: float = 0.002, max_box_frac: float = 1.0,
+          max_detections: int = 100,
           apply: bool = False) -> dict:
     """Detect one class on one frame from the frame's exemplar pool.
 
@@ -189,12 +191,16 @@ def label(frame_id: int, class_id: int, exemplars: List[dict],
     finally:
         jobs.gpu_lock.release()
 
-    # The panel's filters, in the order the batch job applies them (REQ-175):
-    # area floor, then NMS, then the cap on how many survive.
+    # The panel's filters, in the order the batch job applies them (REQ-175/188):
+    # area floor, then ceiling, then NMS, then the cap on how many survive.
     if min_box_frac > 0:
         floor = width * height * min_box_frac
         found = [d for d in found
                  if (d.box[2] - d.box[0]) * (d.box[3] - d.box[1]) >= floor]
+    if 0 < max_box_frac < 1:
+        ceiling = width * height * max_box_frac
+        found = [d for d in found
+                 if (d.box[2] - d.box[0]) * (d.box[3] - d.box[1]) <= ceiling]
     found = deduplicate(found, iou_threshold)
     found.sort(key=lambda d: d.score, reverse=True)
     if max_detections > 0: