{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00023055,0.0001025481,0.0001422044,0.00007981562,0.0001328196,0.0004076817,0.0004332502,0.00009589169,0.00001524726],"category_scores_gemma":[0.00009531927,0.0001049086,0.00005489404,0.00006928538,0.00000902102,0.0001948945,0.0009333507,0.0007862683,0.000009750765],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003812414,"about_ca_system_score_gemma":0.00006136015,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000534125,"about_ca_topic_score_gemma":0.00003386666,"domain_scores_codex":[0.9989076,0.00008183875,0.0001883767,0.0005995919,0.0001019567,0.0001205893],"domain_scores_gemma":[0.9991703,0.0002413701,0.0000691382,0.0003701198,0.00008374125,0.00006533659],"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":[0.0000295583,0.00007984328,0.01482934,0.0004031226,0.0004624927,0.000006799125,0.0175642,0.1638612,0.01472279,0.2343541,0.001620807,0.5520657],"study_design_scores_gemma":[0.00006789194,0.000007071252,0.001003051,0.00003100801,0.00001096857,4.447112e-7,0.00008175819,0.988728,0.0004062789,0.006630658,0.002919394,0.0001134227],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01714575,0.00006807555,0.976722,0.004939802,0.000287907,0.0002097531,0.000002373532,0.0001769113,0.0004474228],"genre_scores_gemma":[0.5309152,0.00001648702,0.468283,0.00009380806,0.0001505572,0.00002765427,0.00002947371,0.00000720575,0.0004766377],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8248668,"threshold_uncertainty_score":0.4278049,"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."}}