{"id":"W3030231421","doi":"10.1145/3397271.3401160","title":"Analyzing and Learning from User Interactions for Search Clarification","year":2020,"lang":"en","type":"preprint","venue":"","topic":"Topic Modeling","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Microsoft (Canada)","funders":"","keywords":"Computer science; Information retrieval; Ranking (information retrieval); Search engine; Presentation (obstetrics); Representation (politics); Web search query; World Wide Web","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":[0.004101255,0.001065005,0.0009610622,0.003029459,0.0004743824,0.001471649,0.0007068743,0.001301655,0.001046334],"category_scores_gemma":[0.0394302,0.0003938914,0.0009829192,0.001666728,0.0005501426,0.003013131,0.001039406,0.001487717,0.0006300657],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008324514,"about_ca_system_score_gemma":0.0007555492,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00285991,"about_ca_topic_score_gemma":0.004471731,"domain_scores_codex":[0.9956656,0.002425742,0.0002831576,0.0007892046,0.000626281,0.0002100559],"domain_scores_gemma":[0.962262,0.03116567,0.002333676,0.001886613,0.001857637,0.0004944801],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.002360761,0.001552995,0.1885495,0.001365228,0.0003352517,0.0004501374,0.009904944,0.03409696,0.05832394,0.003651015,0.004922722,0.6944866],"study_design_scores_gemma":[0.00008109506,0.0007804718,0.1097357,0.00009541984,0.0001158094,0.0002403698,0.001474402,0.8637616,0.01439648,0.005874147,0.003330939,0.0001136441],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7453434,0.001262899,0.2481078,0.0004919456,0.00004182153,0.0004181021,0.001016617,0.00180062,0.001516699],"genre_scores_gemma":[0.9564039,0.0001846437,0.04080575,0.00004908358,0.00003756362,0.000184855,0.001622683,0.00005753785,0.0006541386],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004101255,"threshold_uncertainty_score":0.02168983,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1212220816045111,"score_gpt":0.3391706996031029,"score_spread":0.2179486179985917,"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."}}