{"id":"W3159391311","doi":"","title":"IBM Submission for TAC KBP: EDL 2019 Cascaded Fine-Grained Named Entity Recognition.","year":2019,"lang":"en","type":"article","venue":"Theory and applications of categories","topic":"Topic Modeling","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"IBM; Computer science; Operating system; Programming language; Artificial intelligence; Materials science; Nanotechnology","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":[0.003951451,0.002522966,0.002617155,0.004126472,0.002676031,0.006304191,0.00389192,0.003314389,0.2543046],"category_scores_gemma":[0.02022408,0.001447205,0.00122447,0.004065027,0.0008614787,0.007545063,0.004702553,0.003334184,0.2653048],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002302476,"about_ca_system_score_gemma":0.004457526,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03071802,"about_ca_topic_score_gemma":0.04044182,"domain_scores_codex":[0.996182,0.0006305917,0.000408498,0.0008145519,0.001535124,0.0004292383],"domain_scores_gemma":[0.9869552,0.002278099,0.0002269381,0.002220129,0.007082948,0.001236767],"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":[0.0001754632,0.00003545896,0.0001026477,0.000160431,0.00001956779,0.00008021661,0.00002423169,0.0003186416,0.0007931803,0.000893748,0.9837227,0.01367372],"study_design_scores_gemma":[0.0004700821,0.0001246762,0.001840379,0.0002031779,0.00006218782,0.0003876814,0.0003369832,0.01953822,0.006233978,0.01115316,0.9595406,0.0001088869],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.008278354,0.002082479,0.0612827,0.01113382,0.01773586,0.0009466871,0.732406,0.08873936,0.07739475],"genre_scores_gemma":[0.01322271,0.0003885574,0.03433271,0.0007432065,0.0009735072,0.0003696985,0.8784395,0.007300037,0.0642301],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.2543046,"threshold_uncertainty_score":0.8507336,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01269228310348092,"score_gpt":0.2485888433768967,"score_spread":0.2358965602734158,"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."}}