{"id":"W4413156244","doi":"10.1109/cvpr52734.2025.02522","title":"MATCHA: Towards Matching Anything","year":2025,"lang":"en","type":"article","venue":"","topic":"User Authentication and Security Systems","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Computer science; Matching (statistics); Mathematics; Statistics","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002656861,0.001729278,0.002852624,0.003078677,0.001640684,0.002964212,0.004754929,0.003712503,0.008269744],"category_scores_gemma":[0.008076632,0.0009762252,0.002957252,0.00263322,0.002034739,0.007455557,0.00922608,0.002975508,0.004976097],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001200705,"about_ca_system_score_gemma":0.001589666,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004129526,"about_ca_topic_score_gemma":0.004806428,"domain_scores_codex":[0.9964679,0.000488077,0.0001940842,0.001582815,0.0009642051,0.0003028655],"domain_scores_gemma":[0.9976212,0.0005641012,0.0002386627,0.001091588,0.000303558,0.0001807792],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.000744398,0.0002400889,0.003051927,0.0004856877,0.0003064769,0.0002585988,0.000378289,0.0692344,0.02086891,0.07805761,0.02475288,0.8016207],"study_design_scores_gemma":[0.00007049175,0.0003060267,0.001146615,0.00007484289,0.0001056128,0.0005801303,0.0002008238,0.7961601,0.01529701,0.1602054,0.02578234,0.00007067875],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01303501,0.0005996927,0.976131,0.0002645712,0.0001206363,0.0001374222,0.0005101738,0.006545157,0.002656282],"genre_scores_gemma":[0.2696957,0.0007178555,0.7146857,0.001019211,0.0001815682,0.0004085391,0.003419782,0.001922884,0.007948766],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008269744,"threshold_uncertainty_score":0.02766508,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01216168595823875,"score_gpt":0.2666514030868782,"score_spread":0.2544897171286394,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}