{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007651807,0.0002284876,0.0002770282,0.0000942977,0.00007102723,0.0003283994,0.001228822,0.0001541906,0.0001128198],"category_scores_gemma":[0.0002594177,0.0002009253,0.00007348024,0.0003532619,0.0000723877,0.0007167425,0.0003654327,0.0003249949,0.0006678387],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000106038,"about_ca_system_score_gemma":0.0004538975,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000102121,"about_ca_topic_score_gemma":0.00001360834,"domain_scores_codex":[0.9977333,0.0001307326,0.0003685235,0.0006455101,0.0006306506,0.0004912965],"domain_scores_gemma":[0.9980915,0.00007080256,0.000101371,0.0009588466,0.0003768598,0.0004006734],"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":[0.0006162816,0.0005992707,0.002987974,0.000131459,0.0001545581,0.0005397448,0.02451095,0.001059075,0.01636729,0.3235488,0.03753409,0.5919505],"study_design_scores_gemma":[0.004584339,0.001332085,0.004254844,0.0001973623,0.00005218302,0.000511394,0.0005375033,0.8448822,0.02997568,0.05370995,0.05754199,0.002420388],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3226627,0.0004062275,0.6412211,0.00429307,0.001602608,0.0003518386,0.000006192607,0.0007148474,0.0287415],"genre_scores_gemma":[0.9722361,0.00001103652,0.01923305,0.000215449,0.000154993,0.00000390001,0.000004108494,0.00001334709,0.008128],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8438232,"threshold_uncertainty_score":0.8583938,"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."}}