{"id":"W3015139571","doi":"10.1145/3341105.3374002","title":"Iterative learning to rank from explicit relevance feedback","year":2020,"lang":"en","type":"article","venue":"","topic":"Expert finding and Q&A systems","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Dalhousie University","funders":"Mitacs","keywords":"Computer science; Relevance (law); Rank (graph theory); Learning to rank; Information retrieval; Relevance feedback; Infinite impulse response; Quality (philosophy); Machine learning; Artificial intelligence; Ranking (information retrieval); Image retrieval; Filter (signal processing); Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.00008703614,0.0001216793,0.0001779598,0.00002972671,0.0001195902,0.0002256143,0.0005850455,0.0000401733,0.00005571533],"category_scores_gemma":[0.0001453451,0.000103103,0.00004566886,0.0003544857,0.000006404772,0.0003009656,0.0002065406,0.0001510407,0.001548714],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002235765,"about_ca_system_score_gemma":0.00001798035,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001338738,"about_ca_topic_score_gemma":0.000006963174,"domain_scores_codex":[0.9988418,0.00007935816,0.0001837408,0.0004719929,0.0002090861,0.0002140366],"domain_scores_gemma":[0.9992905,0.0001650556,0.00004565674,0.0002585915,0.00004478728,0.0001954632],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001052323,0.0001017348,0.007459606,0.00004815129,0.0001424483,0.0001515118,0.3914785,0.005818537,0.2323509,0.09451939,0.1768814,0.09094253],"study_design_scores_gemma":[0.001310912,0.0007048165,0.001603393,0.0002127935,0.00000554056,0.000008441665,0.002943761,0.3237772,0.07815707,0.0006693692,0.5895174,0.001089251],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06512965,0.0001442626,0.9066024,0.01395477,0.0003482431,0.0001585535,0.000001530422,0.000520127,0.0131405],"genre_scores_gemma":[0.948837,0.000005347251,0.04338591,0.004522712,0.0002895311,0.00001734708,0.000001885879,0.00001009639,0.002930127],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8837074,"threshold_uncertainty_score":0.9992287,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02671474515489792,"score_gpt":0.2481323306937224,"score_spread":0.2214175855388245,"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."}}