{"id":"W2572992153","doi":"","title":"WaterlooClarke: TREC 2015 Microblog Track.","year":2015,"lang":"en","type":"article","venue":"Text REtrieval Conference","topic":"Optimization and Search Problems","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo; University of Victoria","funders":"","keywords":"Microblogging; Social media; Track (disk drive); Computer science; World Wide Web; Information retrieval; Narrative","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":[0.007988935,0.004885445,0.003477023,0.007746038,0.005613895,0.008823962,0.0051493,0.003188176,0.1196277],"category_scores_gemma":[0.01236805,0.001781777,0.001018511,0.006711323,0.001667836,0.00921065,0.002552829,0.004829469,0.09634684],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01297235,"about_ca_system_score_gemma":0.01416922,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.4552945,"about_ca_topic_score_gemma":0.7059357,"domain_scores_codex":[0.9958507,0.0007048568,0.0002694079,0.0006292471,0.002044363,0.0005014244],"domain_scores_gemma":[0.9856163,0.001513203,0.0004846873,0.001318596,0.008560801,0.002506438],"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":[0.00006063212,0.00008763825,0.0001179029,0.0001219207,0.00001701773,0.00001699073,0.00001648626,0.0001584086,0.0003504287,0.0001583143,0.9922974,0.006596931],"study_design_scores_gemma":[0.001039333,0.000339891,0.01294559,0.0003762744,0.0001363901,0.0001280511,0.0003393255,0.01127504,0.006132897,0.003363989,0.9636944,0.0002287345],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.01115639,0.01449767,0.009045472,0.02029898,0.008709198,0.003763945,0.7637238,0.03639557,0.132409],"genre_scores_gemma":[0.01545247,0.002264907,0.01081654,0.002234901,0.001048813,0.001199926,0.821013,0.002182941,0.1437865],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.4552945,"threshold_uncertainty_score":0.9052888,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06991052720693956,"score_gpt":0.2990576984579198,"score_spread":0.2291471712509802,"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."}}