{"id":"W2962770186","doi":"10.18653/v1/d15-1179","title":"Fast, Flexible Models for Discovering Topic Correlation across Weakly-Related Collections","year":2015,"lang":"en","type":"preprint","venue":"","topic":"Topic Modeling","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; John Templeton Foundation; National Science Foundation","keywords":"Computer science; Correlation; Information retrieval; Mathematics","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.012213,0.002040555,0.003261485,0.004855583,0.002013125,0.00521888,0.005731986,0.00298585,0.001985067],"category_scores_gemma":[0.03701021,0.002181296,0.003010863,0.006989216,0.001526503,0.01131763,0.004384636,0.006540492,0.003154216],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001724937,"about_ca_system_score_gemma":0.002819288,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009753645,"about_ca_topic_score_gemma":0.01588571,"domain_scores_codex":[0.9940702,0.002265349,0.0004439308,0.001923162,0.0008610425,0.0004362363],"domain_scores_gemma":[0.9730987,0.01820873,0.001532002,0.004408379,0.002059135,0.0006929367],"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":[0.00160023,0.0008995953,0.03199399,0.001218429,0.001535043,0.0005111797,0.002186466,0.2821252,0.009523093,0.05102208,0.03802875,0.5793559],"study_design_scores_gemma":[0.00005842756,0.00006744612,0.001975949,0.00004757013,0.0001167213,0.0001473548,0.0001625107,0.9404824,0.001168223,0.0521129,0.00361381,0.00004666482],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03139471,0.001904104,0.9609155,0.0007505859,0.0001231149,0.0002447702,0.001592969,0.002072631,0.001001658],"genre_scores_gemma":[0.4938694,0.003297227,0.4789915,0.0006290411,0.001297381,0.001315306,0.01336532,0.0009952975,0.006239607],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.012213,"threshold_uncertainty_score":0.06458926,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06405536907975934,"score_gpt":0.3114599321620022,"score_spread":0.2474045630822428,"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."}}