{"id":"W2806148218","doi":"","title":"CMU-LTI at KBP 2015 Event Track.","year":2015,"lang":"en","type":"article","venue":"Theory and applications of categories","topic":"Topic Modeling","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Track (disk drive); Computer science; Event (particle physics); Physics; Operating system","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.007046612,0.003058644,0.002871518,0.00330327,0.002582482,0.009046711,0.004320466,0.003498887,0.3968101],"category_scores_gemma":[0.02689603,0.001087275,0.001176724,0.005089399,0.0005708454,0.01008403,0.005213622,0.004467943,0.3854857],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00309476,"about_ca_system_score_gemma":0.003054455,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01762021,"about_ca_topic_score_gemma":0.01873323,"domain_scores_codex":[0.9957753,0.001129582,0.0002153275,0.0008435758,0.00151944,0.0005168031],"domain_scores_gemma":[0.9891562,0.002598144,0.0003667028,0.002176926,0.003206732,0.002495201],"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":[0.0001503208,0.00002944446,0.0001145386,0.0001016205,0.000006934398,0.00002409323,0.00002596114,0.0001841896,0.0002608121,0.002035217,0.9883569,0.008710041],"study_design_scores_gemma":[0.0002294836,0.0001220566,0.001158802,0.0001604466,0.00001990102,0.00008267305,0.0001199813,0.008975287,0.002422306,0.0104157,0.9762314,0.00006204227],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.003930986,0.002513629,0.06665031,0.01817061,0.01175051,0.0009354184,0.589245,0.1559736,0.1508299],"genre_scores_gemma":[0.02109517,0.00110533,0.03105728,0.00212361,0.002926911,0.0007763875,0.7745776,0.01894731,0.1473904],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.3968101,"threshold_uncertainty_score":0.8603771,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02027633939437049,"score_gpt":0.2751628017077324,"score_spread":0.254886462313362,"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."}}