{"id":"W2904945528","doi":"10.1609/aaai.v33i01.33014570","title":"Cost-Sensitive Learning to Rank","year":2019,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Multi-Criteria Decision Making","field":"Decision Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Simon Fraser University","funders":"","keywords":"Rank (graph theory); Learning to rank; Ranking (information retrieval); Computer science; Machine learning; Artificial intelligence; Risk analysis (engineering); Business; Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.003641642,0.0003050915,0.0005654796,0.000476561,0.0002522824,0.0007216841,0.002507508,0.0001289493,0.00175031],"category_scores_gemma":[0.01283182,0.0002046432,0.0002352527,0.001717942,0.0002187766,0.0004342933,0.0007863184,0.0005312552,0.007069965],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006999481,"about_ca_system_score_gemma":0.00009779931,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003260044,"about_ca_topic_score_gemma":0.00001183221,"domain_scores_codex":[0.994864,0.0000893909,0.001250004,0.0009796915,0.002305246,0.0005116278],"domain_scores_gemma":[0.9944881,0.001330231,0.0007260963,0.0005423135,0.002708265,0.0002050443],"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":[0.0007445591,0.0001371368,0.003429189,0.00001528778,0.00002118923,0.000001742606,0.007383835,0.001614391,0.3134778,0.2204362,0.001204425,0.4515342],"study_design_scores_gemma":[0.0001048962,0.0004638481,0.003236673,0.0005019607,0.00001764046,0.00001140747,0.01653683,0.0706882,0.768602,0.1329839,0.006254053,0.0005986366],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.950375,0.000006428198,0.006188695,0.003839937,0.001244357,0.001245733,0.000009993021,0.00007255196,0.03701726],"genre_scores_gemma":[0.9935337,0.000005116722,0.001757681,0.0006047065,0.00009198252,0.00002300454,3.073998e-7,0.00002397337,0.003959539],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4551241,"threshold_uncertainty_score":0.9991622,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3142401984445709,"score_gpt":0.4377200395174314,"score_spread":0.1234798410728606,"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."}}