{"id":"W7011720527","doi":"","title":"NeurIPS’22 Cross-Domain MetaDL Challenge:Results and lessons learned","year":2023,"lang":"en","type":"article","venue":"TU/e Research Portal","topic":"Folate and B Vitamins Research","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada)","funders":"Agence Nationale de la Recherche","keywords":"Task (project management); Competition (biology); Domain (mathematical analysis); Baseline (sea); Adaptation (eye); Conjunction (astronomy); Training set","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":[0.01699282,0.006623002,0.003823492,0.002385769,0.001988244,0.006766911,0.005188049,0.006354088,0.01294269],"category_scores_gemma":[0.02066184,0.0007941479,0.002862929,0.001990966,0.001699359,0.007032543,0.008460299,0.006408009,0.01485832],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002903036,"about_ca_system_score_gemma":0.00432929,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01405123,"about_ca_topic_score_gemma":0.02176961,"domain_scores_codex":[0.9902733,0.003100667,0.0004953571,0.002247798,0.002494036,0.001388898],"domain_scores_gemma":[0.9875485,0.003447347,0.0002482587,0.002493703,0.003651473,0.002610678],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.002471879,0.002680673,0.003472615,0.002732422,0.0008487477,0.0005249031,0.0002386968,0.0365255,0.006210865,0.004661533,0.6933507,0.2462814],"study_design_scores_gemma":[0.004411255,0.004657186,0.01153283,0.001379398,0.0007780486,0.001725237,0.002083643,0.3487109,0.03849605,0.0438814,0.5416897,0.0006544011],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2429349,0.06557562,0.2537516,0.04345351,0.03764491,0.003372006,0.1003824,0.09395617,0.158929],"genre_scores_gemma":[0.3241596,0.007267782,0.2374297,0.01488257,0.003303203,0.001691154,0.3228671,0.01031941,0.07807954],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01699282,"threshold_uncertainty_score":0.08986771,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2790812992901041,"score_gpt":0.5148689908232,"score_spread":0.235787691533096,"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."}}