{"id":"W3031309641","doi":"","title":"Temporal Histories of Epidemic Events (THEE): A Case Study in Temporal Annotation for Public Health","year":2020,"lang":"en","type":"article","venue":"Language Resources and Evaluation","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"Public Health Agency of Canada; University of Toronto","funders":"","keywords":"Annotation; Computer science; Metadata; Domain (mathematical analysis); Event (particle physics); Public domain; Information retrieval; Process (computing); Temporal annotation; Style (visual arts); Natural language processing; Artificial intelligence; World Wide Web; History; Natural language","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001103155,0.00007124977,0.0001502658,0.0000370577,0.00004505162,0.000006926432,0.0000480539,0.00006046314,0.000004306301],"category_scores_gemma":[0.0005188499,0.00006129262,0.00002790007,0.00008931877,0.00003601295,0.00000386181,0.00002852492,0.00004009085,1.383455e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001821607,"about_ca_system_score_gemma":0.00006735195,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0005714019,"about_ca_topic_score_gemma":0.0007684382,"domain_scores_codex":[0.9990916,0.0002146451,0.0002422631,0.0001971102,0.0001356061,0.0001187818],"domain_scores_gemma":[0.9996285,0.00002918048,0.0001415449,0.00008566205,0.00005264641,0.00006242514],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"qualitative","study_design_scores_codex":[0.000332516,0.0003416039,0.4408696,0.00028981,0.00008480895,0.00003073954,0.1195966,0.00008356573,0.007277812,0.000009623271,0.0007852736,0.430298],"study_design_scores_gemma":[0.02077325,0.01954329,0.1578536,0.0001469636,0.0002154439,0.0002783679,0.6555247,0.07044589,0.001830995,0.0002447634,0.07191181,0.001230925],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9942805,0.002748109,0.0007471386,0.001705402,0.0000294814,0.0004605699,0.00001325954,0.000008119857,0.00000747237],"genre_scores_gemma":[0.9986964,0.00001145492,0.0008579012,0.0001667875,0.00007841443,0.00004650302,0.0001194574,0.000006553536,0.00001656411],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.535928,"threshold_uncertainty_score":0.2499441,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1061119531422761,"score_gpt":0.3841453758342169,"score_spread":0.2780334226919409,"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."}}