{"id":"W2572043595","doi":"","title":"WaterlooClarke: TREC 2015 Total Recall Track","year":2015,"lang":"en","type":"article","venue":"Text REtrieval Conference","topic":"Topic Modeling","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Recall; Track (disk drive); Cluster analysis; Selection (genetic algorithm); Process (computing); Precision and recall; Information retrieval; Data mining; Artificial intelligence","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":[0.01448561,0.002032948,0.002095816,0.007356036,0.003616372,0.006920555,0.003400273,0.002403941,0.02369987],"category_scores_gemma":[0.0205852,0.0007930917,0.0009092933,0.004083272,0.001361721,0.004411845,0.002564009,0.003380972,0.01586404],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01200396,"about_ca_system_score_gemma":0.01634326,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.271963,"about_ca_topic_score_gemma":0.5217234,"domain_scores_codex":[0.9910861,0.001628319,0.0004408884,0.0007808627,0.005269765,0.0007940609],"domain_scores_gemma":[0.9713995,0.002266387,0.000967263,0.002253056,0.02098079,0.002132892],"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":[0.0001845378,0.0001371379,0.0007162642,0.0003116088,0.00006144543,0.00003390732,0.00004166469,0.0009562999,0.002010691,0.0009522534,0.9629249,0.03166927],"study_design_scores_gemma":[0.0005675825,0.0007032862,0.02162011,0.0003693745,0.0002089898,0.0002502717,0.0002613504,0.02540704,0.01967499,0.004582406,0.9260593,0.0002954387],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.04729506,0.03558841,0.0710213,0.04083132,0.02050482,0.007762236,0.4767047,0.04615949,0.2541327],"genre_scores_gemma":[0.09328292,0.005436054,0.04583829,0.004940753,0.00193638,0.002325764,0.5402797,0.002435003,0.3035253],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.271963,"threshold_uncertainty_score":0.54076,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0752741043559129,"score_gpt":0.2872297986682088,"score_spread":0.2119556943122959,"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."}}