{"id":"W2970434547","doi":"","title":"Entity and Event Extraction from Scratch Using Minimal Training Data.","year":2018,"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":"Scratch; Computer science; Event (particle physics); Artificial intelligence; Extraction (chemistry); Training set; Natural language processing; Programming language; Chromatography","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":[0.001762571,0.001613252,0.001239622,0.004281755,0.001237873,0.002195429,0.002609085,0.001818047,0.008014658],"category_scores_gemma":[0.01298885,0.0007680905,0.00181421,0.004397083,0.0005659687,0.005770444,0.002800012,0.00235238,0.01151036],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006729538,"about_ca_system_score_gemma":0.002726221,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005876835,"about_ca_topic_score_gemma":0.01305869,"domain_scores_codex":[0.998031,0.0003919073,0.000197248,0.0009028395,0.0002954953,0.0001814832],"domain_scores_gemma":[0.993495,0.003303585,0.0002472939,0.001893413,0.0008715487,0.0001891523],"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":[0.0007009722,0.0006634981,0.01063825,0.001645846,0.0003977556,0.0008963314,0.0005788075,0.01722822,0.02528021,0.01103872,0.09097542,0.8399559],"study_design_scores_gemma":[0.0001833788,0.0004672464,0.02495071,0.0008495377,0.0009099562,0.002504423,0.001888758,0.5946143,0.05742871,0.1086483,0.2073611,0.000193561],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.03557865,0.001967328,0.9012777,0.0008102931,0.0004103504,0.0007429458,0.03518319,0.0152421,0.008787399],"genre_scores_gemma":[0.2208465,0.001341499,0.5851257,0.0003454944,0.0002911094,0.001117253,0.1822916,0.0008279941,0.007812914],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008014658,"threshold_uncertainty_score":0.02681166,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05075145715156108,"score_gpt":0.3190120562127388,"score_spread":0.2682605990611777,"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."}}