fix: tighten near-duplicate detection to <1 cm absolute difference per axis

Replaces percentage-based threshold (15%) with strict absolute tolerance:
two dimension groups are flagged only if all three sorted dimensions
differ by less than 1 cm. Targets likely data entry typos.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

src/components/DimensionsView.tsx

@@ -110,14 +110,14 @@ export function DimensionsView({ data, headers, onEdit, onSaveRow, onCaptureStat
   }, [groups, showOnlyInconsistent]);
 
   const nearDuplicateClusters = useMemo((): NearDuplicateCluster[] => {
-    // Two groups are "similar" if every sorted dimension pair differs by ≤15%
+    // Two groups are "similar" if every sorted dimension pair differs by < 1 cm absolute
     // (keys are already sorted ascending, e.g. "15x20x29")
-    const THRESHOLD = 0.15;
+    const MAX_DIFF_CM = 1;
 
-    const maxRelativeDiff = (keyA: string, keyB: string): number => {
+    const maxAbsDiff = (keyA: string, keyB: string): number => {
       const a = keyA.split('x').map(Number);
       const b = keyB.split('x').map(Number);
-      return Math.max(...a.map((v, i) => Math.abs(v - b[i]) / Math.max(v, b[i], 0.001)));
+      return Math.max(...a.map((v, i) => Math.abs(v - b[i])));
     };
 
     const validGroups = groups.filter(g => g.volume > 0);
@@ -135,8 +135,8 @@ export function DimensionsView({ data, headers, onEdit, onSaveRow, onCaptureStat
         const b = validGroups[j];
         if (assignedKeys.has(b.key)) continue;
 
-        // b must be similar to every group already in the cluster
+        // b must be within 1 cm on every axis of every group already in the cluster
-        const isSimilar = cluster.every(g => maxRelativeDiff(g.key, b.key) <= THRESHOLD);
+        const isSimilar = cluster.every(g => maxAbsDiff(g.key, b.key) < MAX_DIFF_CM);
         if (isSimilar) {
           cluster.push(b);
           assignedKeys.add(b.key);
@@ -145,11 +145,11 @@ export function DimensionsView({ data, headers, onEdit, onSaveRow, onCaptureStat
 
       if (cluster.length >= 2) {
         const volumes = cluster.map(g => g.volume);
-        // Max dimension-wise diff across all pairs in the cluster
+        // Max absolute diff (cm) across all pairs in the cluster
         let maxDiffPct = 0;
         for (let x = 0; x < cluster.length; x++) {
           for (let y = x + 1; y < cluster.length; y++) {
-            maxDiffPct = Math.max(maxDiffPct, maxRelativeDiff(cluster[x].key, cluster[y].key) * 100);
+            maxDiffPct = Math.max(maxDiffPct, maxAbsDiff(cluster[x].key, cluster[y].key));
           }
         }
         clusters.push({ groups: cluster, volumes, maxDiffPct });
@@ -254,7 +254,7 @@ export function DimensionsView({ data, headers, onEdit, onSaveRow, onCaptureStat
             <Link2 className="w-4 h-4 text-violet-400" />
             <span className="text-sm font-semibold text-violet-300">Possible data entry errors</span>
             <span className="text-xs text-slate-500 bg-slate-900 px-2 py-0.5 rounded-full border border-slate-700">
-              {nearDuplicateClusters.length} cluster{nearDuplicateClusters.length !== 1 ? 's' : ''} with similar dimensions (≤15% per axis)
+              {nearDuplicateClusters.length} cluster{nearDuplicateClusters.length !== 1 ? 's' : ''} with dimensions differing &lt;1 cm per axis
             </span>
           </div>
           {nearDuplicateClusters.map((cluster, ci) => (
@@ -276,7 +276,7 @@ export function DimensionsView({ data, headers, onEdit, onSaveRow, onCaptureStat
                     </span>
                   ))}
                   <span className="text-xs text-violet-400/70">
-                    — max axis diff {cluster.maxDiffPct.toFixed(1)}%
+                    — max diff {cluster.maxDiffPct.toFixed(1)} cm
                   </span>
                 </div>
               </button>