diff --git a/scripts/pick_barrier.py b/scripts/pick_barrier.py
index 06b4431..fc147d6 100644
--- a/scripts/pick_barrier.py
+++ b/scripts/pick_barrier.py
@@ -44,7 +44,7 @@ import pandas as pd
 
 from config import DATA_METADATA, INVENTORY_CSV, VIDEO_INFO_TSV
 from detect_barrier_opening import (
-    SEARCH_END_S, STEP_S, WINDOW_S,
+    STEP_S, WINDOW_S,
     per_frame_distance, sliding_mean,
 )
 
@@ -58,8 +58,9 @@ DB_NAME_RE = re.compile(
 
 GRID_ROWS, GRID_COLS = 6, 10
 N_THUMBS = GRID_ROWS * GRID_COLS  # 60
-COARSE_SPAN_S = SEARCH_END_S       # 0..300s, ~5s spacing
-FINE_SPAN_S = 12.0                 # ±6s around coarse pick → ~0.2s spacing
+DEFAULT_COARSE_SPAN_S = 600.0  # 0..10 min, ~10 s spacing — covers late-opening videos
+FINE_SPAN_S = 12.0             # ±6 s around coarse pick → ~0.2 s spacing
+AUTO_SEARCH_END_S = 600.0      # how far the auto-detector scans for its suggestion
 
 
 def auto_suggest(db_path: Path) -> float | None:
@@ -75,7 +76,7 @@ def auto_suggest(db_path: Path) -> float | None:
         except Exception:
             continue
         dist = per_frame_distance(df)
-        smean = sliding_mean(dist, WINDOW_S, STEP_S, SEARCH_END_S)
+        smean = sliding_mean(dist, WINDOW_S, STEP_S, AUTO_SEARCH_END_S)
         pad = max(1, int(WINDOW_S / STEP_S))
         if len(smean) < 2 * pad + 1:
             continue
@@ -218,17 +219,19 @@ def pick_for_video(
     machine_name: str,
     session_date: str,
     session_time: str,
+    coarse_span_s: float = DEFAULT_COARSE_SPAN_S,
 ) -> dict | str | None:
     """Run the two-stage thumbnail picker. Return dict, 'skip', or 'quit'."""
     auto_t = auto_suggest(db_path) if db_path else None
     print(f"  auto-suggest: {f'{auto_t:.1f}s' if auto_t else '(none)'}")
 
-    # Stage 1: coarse grid centred on auto-suggest (or 150 s default).
-    coarse_center = auto_t if auto_t is not None else COARSE_SPAN_S / 2
-    title_coarse = f"COARSE  {machine_name}  {session_date} {session_time}  ·  spanning 5 min"
+    # Stage 1: coarse grid centred on auto-suggest, or middle of span.
+    coarse_center = auto_t if auto_t is not None else coarse_span_s / 2
+    title_coarse = (f"COARSE  {machine_name}  {session_date} {session_time}  "
+                    f"·  spanning {coarse_span_s/60:.0f} min")
     while True:
         coarse_t, action = show_thumbnail_grid(
-            video_path, coarse_center, COARSE_SPAN_S, title_coarse
+            video_path, coarse_center, coarse_span_s, title_coarse
         )
         if action == "skip":
             return "skip"
@@ -294,6 +297,8 @@ def main() -> None:
                         help="only process the first N videos")
     parser.add_argument("--db", type=Path, default=None,
                         help="annotate this specific tracking DB only")
+    parser.add_argument("--coarse-span", type=float, default=DEFAULT_COARSE_SPAN_S,
+                        help=f"coarse-grid time span in seconds (default {DEFAULT_COARSE_SPAN_S:.0f})")
     args = parser.parse_args()
 
     OUT_CSV.parent.mkdir(parents=True, exist_ok=True)
@@ -357,7 +362,8 @@ def main() -> None:
         prefix = f"[{i}/{len(queue)}] {machine_name} {session_date} {session_time}"
         print(f"\n{prefix}")
 
-        result = pick_for_video(video, db, machine_name, session_date, session_time)
+        result = pick_for_video(video, db, machine_name, session_date, session_time,
+                                coarse_span_s=args.coarse_span)
 
         if result is None or result == "skip":
             skipped += 1