{"id":"W4396723302","doi":"10.1145/3589334.3645474","title":"Whole Page Unbiased Learning to Rank","year":2024,"lang":"en","type":"article","venue":"","topic":"Machine Learning and Data Classification","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Wilfrid Laurier University","funders":"","keywords":"Computer science; Learning to rank; Information retrieval; Ranking (information retrieval)","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003150011,0.00006406743,0.00005629817,0.0001051091,0.00008065067,0.0004470111,0.0003599259,0.00002335987,0.00008799779],"category_scores_gemma":[0.0001016283,0.00005239641,0.00002696209,0.0004514262,0.000006148651,0.0002695803,0.0001168021,0.0001673718,0.003244458],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001601569,"about_ca_system_score_gemma":0.00002823891,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00004058494,"about_ca_topic_score_gemma":0.000006175834,"domain_scores_codex":[0.999256,0.00006037662,0.00009521731,0.0003085039,0.0001417552,0.0001382001],"domain_scores_gemma":[0.9994923,0.00009267209,0.00001048478,0.0003109758,0.00001708117,0.00007647726],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.000003272959,0.00002402084,0.0005186682,0.00003806454,0.00001258824,0.00003218082,0.001688871,0.001499985,0.02436597,0.2088216,0.05547366,0.7075211],"study_design_scores_gemma":[0.00004236158,0.00003392142,0.001857837,0.000016583,0.000001443681,0.000003370058,0.0000255449,0.3605503,0.0003265488,0.0002483096,0.6368117,0.00008212389],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003942718,0.00008091651,0.9532195,0.01310459,0.0002603077,0.00006032524,8.751459e-7,0.001063856,0.02826688],"genre_scores_gemma":[0.9486474,0.000003725869,0.02560365,0.000637087,0.00007551463,0.000009259248,0.00001729854,0.000007611039,0.02499845],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9447047,"threshold_uncertainty_score":0.9975317,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01761870828984522,"score_gpt":0.2787650035453022,"score_spread":0.261146295255457,"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."}}