{"id":"W2554197048","doi":"","title":"WaterlooClarke: TREC 2015 Contextual Suggestion Track","year":2015,"lang":"en","type":"article","venue":"Text REtrieval Conference","topic":"Topic Modeling","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Task (project management); Track (disk drive); Point of interest; Point (geometry); Contextual design; Information retrieval; Human–computer interaction; Artificial intelligence; Machine learning","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":[0.00598183,0.001565764,0.001587644,0.003481928,0.002932035,0.0027243,0.002284323,0.002067665,0.03226847],"category_scores_gemma":[0.01288827,0.0006318673,0.0004598303,0.003416764,0.0008555455,0.00378522,0.001990313,0.002431729,0.01802067],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005777915,"about_ca_system_score_gemma":0.009137995,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.280229,"about_ca_topic_score_gemma":0.5432101,"domain_scores_codex":[0.9965132,0.001008447,0.0001846798,0.0006000794,0.001351277,0.0003422782],"domain_scores_gemma":[0.9899858,0.002003706,0.0003459099,0.001581225,0.005304062,0.0007793062],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002810958,0.0001900129,0.0009899621,0.0004195555,0.00004610827,0.0000498048,0.0001629959,0.0007563881,0.004781559,0.0009973402,0.9383735,0.05295167],"study_design_scores_gemma":[0.0003988064,0.0004052148,0.01005919,0.0001941685,0.0001088773,0.0001311336,0.0004644173,0.02201984,0.01279334,0.00229985,0.95091,0.0002150475],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"methods","genre_scores_codex":[0.04013655,0.01397053,0.1071658,0.01608598,0.004623603,0.006298276,0.5410218,0.176761,0.09393649],"genre_scores_gemma":[0.08519561,0.002346451,0.176748,0.00393392,0.0007517428,0.002245221,0.6378281,0.003710562,0.08724046],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.280229,"threshold_uncertainty_score":0.5571958,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0765092666209366,"score_gpt":0.2924376573472113,"score_spread":0.2159283907262747,"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."}}