{"id":"W2181916342","doi":"","title":"CornPittMich Sentiment Slot-Filling System at TAC 2013","year":2013,"lang":"en","type":"article","venue":"Theory and applications of categories","topic":"Topic Modeling","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Computer science; Relevance (law); Task (project management); Sentence; Sentiment analysis; Process (computing); Knowledge base; Information retrieval; Measure (data warehouse); Base (topology); Architecture; Natural language processing; Document retrieval; Artificial intelligence; Data mining; Mathematics; Programming language","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":[0.002386071,0.0009802454,0.001099481,0.002040558,0.001543865,0.002387858,0.001952707,0.001076186,0.01851797],"category_scores_gemma":[0.004242925,0.0005343268,0.0006014641,0.001795586,0.0004339327,0.002851041,0.001563152,0.001646615,0.01386496],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001818607,"about_ca_system_score_gemma":0.003402024,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02483614,"about_ca_topic_score_gemma":0.02727017,"domain_scores_codex":[0.998468,0.0003110593,0.0001027614,0.0003381573,0.0006050027,0.0001750946],"domain_scores_gemma":[0.9982626,0.0002322579,0.00006456955,0.0002940347,0.0009807441,0.0001658607],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.001217667,0.0003783798,0.001757153,0.0002959912,0.00009828952,0.0005142455,0.0006830256,0.004297352,0.0403638,0.009073727,0.6080947,0.3332257],"study_design_scores_gemma":[0.0008560925,0.0004735463,0.005714698,0.0001478331,0.0002148626,0.0007860277,0.0007512205,0.2733895,0.08846065,0.0234339,0.605453,0.000318633],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.08248915,0.001616553,0.4774747,0.003425366,0.001251518,0.002260569,0.06009932,0.2813405,0.09004241],"genre_scores_gemma":[0.194602,0.000462649,0.6195952,0.001081457,0.0003168466,0.001456664,0.1314508,0.007348554,0.04368572],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02483614,"threshold_uncertainty_score":0.06194878,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008732002905667332,"score_gpt":0.2184206274297902,"score_spread":0.2096886245241228,"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."}}