feat: weighted criteria in AI ranking, z-score normalization, threshold advancement, CSV export

- Add criteriaWeights to EvaluationConfig for per-criterion weight assignment (0-10)
- Rewrite ai-ranking service: fetch eval form criteria, compute per-criterion averages,
  z-score normalize juror scores to correct grading bias, send weighted criteria to AI
- Update AI prompts with criteria_definitions and per-project criteria_scores
- compositeScore uses weighted criteria when configured, falls back to globalScore
- Add collapsible ranking config section to dashboard (criteria text + weight sliders)
- Move rankingCriteria textarea from eval config tab to ranking dashboard
- Store criteriaWeights in ranking snapshot parsedRulesJson for audit
- Enhance projectScores CSV export with per-criterion averages, category, country
- Add Export CSV button to ranking dashboard header
- Add threshold-based advancement mode (decimal score threshold, e.g. 6.5)
  alongside existing top-N mode in advance dialog

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

src/types/competition-configs.ts

@@ -142,6 +142,9 @@ export const EvaluationConfigSchema = z.object({
   rankingEnabled: z.boolean().default(false),
   rankingCriteria: z.string().optional(),
   autoRankOnComplete: z.boolean().default(false),
+
+  // Ranking (Phase 2) — per-criterion weights for AI ranking
+  criteriaWeights: z.record(z.string(), z.number().min(0).max(10)).optional(),
 })
 
 export type EvaluationConfig = z.infer<typeof EvaluationConfigSchema>