{"id":"W2807570555","doi":"","title":"Cornell Belief and Sentiment System at TAC 2016.","year":2016,"lang":"en","type":"article","venue":"Theory and applications of categories","topic":"Topic Modeling","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Computer science; Artificial intelligence; Natural language processing","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":[],"consensus_categories":[],"category_scores_codex":[0.003159144,0.0008556116,0.0005565695,0.00208866,0.0009832358,0.001688108,0.001149251,0.001034319,0.0169684],"category_scores_gemma":[0.01194916,0.0003200373,0.0004556452,0.001410675,0.0002565567,0.002837237,0.001159484,0.001514542,0.0134615],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002120805,"about_ca_system_score_gemma":0.001806297,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06215487,"about_ca_topic_score_gemma":0.1144866,"domain_scores_codex":[0.9988187,0.000379276,0.00006649265,0.0002197532,0.000394278,0.0001216208],"domain_scores_gemma":[0.9967308,0.0007802357,0.0001135776,0.0005854085,0.001459753,0.0003303211],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0006048761,0.0003582642,0.01033468,0.0001618567,0.00008723637,0.0000940872,0.0002774387,0.004566176,0.001152558,0.002229125,0.9082897,0.07184405],"study_design_scores_gemma":[0.000533456,0.0003346894,0.0407897,0.00014885,0.0001911309,0.0001155506,0.0005866418,0.4252815,0.009350608,0.01490308,0.5075595,0.0002052953],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"methods","genre_scores_codex":[0.1880259,0.001239894,0.04204134,0.006263147,0.002428629,0.001162256,0.5767321,0.08628851,0.09581827],"genre_scores_gemma":[0.3173656,0.0002435338,0.04495462,0.000560309,0.0004160076,0.0007159774,0.5949658,0.002152624,0.0386256],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.06215487,"threshold_uncertainty_score":0.1235862,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008785457471002251,"score_gpt":0.2151886304630239,"score_spread":0.2064031729920217,"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."}}