{"id":"W2806375004","doi":"","title":"Modeling Event Extraction via Multilingual Data Sources.","year":2015,"lang":"en","type":"article","venue":"Theory and applications of categories","topic":"Topic Modeling","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Computer science; Event data; Extraction (chemistry); Event (particle physics); Data extraction; Natural language processing; Information extraction; Chromatography; Chemistry; Physics; MEDLINE","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":[0.005090906,0.001254411,0.001051449,0.005239279,0.0009449545,0.00442834,0.001955194,0.001350555,0.003309059],"category_scores_gemma":[0.02539173,0.0009498033,0.002491622,0.006558757,0.0005886949,0.007841467,0.00318693,0.001918602,0.00223298],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001502194,"about_ca_system_score_gemma":0.002213879,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01254828,"about_ca_topic_score_gemma":0.01758039,"domain_scores_codex":[0.9960684,0.001706134,0.0004205065,0.000883419,0.000710313,0.0002113291],"domain_scores_gemma":[0.988325,0.008721796,0.0006703237,0.0009977663,0.001109281,0.0001758039],"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":[0.001294355,0.0004061488,0.0265634,0.002497423,0.001618375,0.001636747,0.002894827,0.2262648,0.006667211,0.1615849,0.04908957,0.5194822],"study_design_scores_gemma":[0.00006328311,0.0000471445,0.002697827,0.0002026551,0.0002844172,0.0003607155,0.0004391435,0.8008928,0.004494092,0.1554101,0.0350503,0.00005751431],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0128683,0.002305777,0.9714556,0.0009845357,0.0002170759,0.0002057668,0.005731155,0.003000639,0.003231127],"genre_scores_gemma":[0.3789668,0.002875303,0.5809298,0.0003441404,0.0004167365,0.0006174851,0.03058771,0.0006971206,0.004564795],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01254828,"threshold_uncertainty_score":0.0269236,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04752959636667484,"score_gpt":0.3167967502811873,"score_spread":0.2692671539145124,"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."}}