{"id":"W6891733196","doi":"10.48448/88ta-w784","title":"MatRank: Text Re-ranking by Latent Preference Matrix","year":2022,"lang":"en","type":"other","venue":"Open MIND","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Ranking (information retrieval); Preference; Key (lock); Precision and recall; Recall; Preference learning; Learning to rank; Conjunction (astronomy)","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":[0.001295213,0.002385487,0.001507384,0.004018808,0.0008309096,0.001641447,0.001956169,0.001051938,0.007661117],"category_scores_gemma":[0.007088615,0.0004722225,0.001073898,0.003725574,0.00054399,0.003677632,0.001480765,0.001425477,0.008215049],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008940264,"about_ca_system_score_gemma":0.001959816,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009270435,"about_ca_topic_score_gemma":0.02751729,"domain_scores_codex":[0.99812,0.0004632755,0.0001227608,0.0004585767,0.0006791021,0.0001562374],"domain_scores_gemma":[0.9977102,0.000632069,0.0002281223,0.0007168589,0.0005750165,0.000137666],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0005621837,0.0005737016,0.004109438,0.000737942,0.0002729053,0.0002979182,0.0001545066,0.04914955,0.0130362,0.01077992,0.2005184,0.7198073],"study_design_scores_gemma":[0.0002504656,0.0003255429,0.001834309,0.000046554,0.00008014758,0.0005140275,0.000105527,0.9144383,0.01458099,0.0330449,0.03465288,0.000126362],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.05232385,0.004525275,0.8398172,0.001245454,0.0008627619,0.0007888564,0.01900292,0.07010464,0.0113291],"genre_scores_gemma":[0.2865577,0.001987991,0.6085829,0.0007164251,0.0006351016,0.0005654723,0.06526069,0.002323405,0.0333704],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009270435,"threshold_uncertainty_score":0.02562892,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06555949826178818,"score_gpt":0.3229313012107957,"score_spread":0.2573718029490075,"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."}}