{"id":"W6920629036","doi":"10.6084/m9.figshare.26564418","title":"Additional file 1 of Entity and relation extraction from clinical case reports of COVID-19: a natural language processing approach","year":2024,"lang":"en","type":"article","venue":"Open MIND","topic":"Topic Modeling","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Public Health Ontario; University of Toronto","funders":"","keywords":"Table (database); Relationship extraction; Natural language; Named-entity recognition; Relation (database); Information extraction; Snippet; Notation","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002304248,0.001697325,0.001255758,0.00323728,0.000794515,0.001570254,0.002057892,0.001472015,0.7538056],"category_scores_gemma":[0.0274505,0.0006057038,0.00114238,0.002938941,0.0003229943,0.001865781,0.001745188,0.001050692,0.1628683],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001275546,"about_ca_system_score_gemma":0.002545789,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006355706,"about_ca_topic_score_gemma":0.01332829,"domain_scores_codex":[0.9991341,0.0001599528,0.0002223239,0.0002359666,0.0001574526,0.00009020416],"domain_scores_gemma":[0.9803261,0.01470184,0.001054528,0.001110156,0.002294871,0.000512521],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004456558,0.00008589052,0.002429423,0.003192113,0.00003869399,0.0002076759,0.00008098161,0.0003806686,0.0002895998,0.000397537,0.978269,0.01418273],"study_design_scores_gemma":[0.003075812,0.0003765328,0.02492906,0.003703201,0.000204682,0.001817064,0.0009031657,0.004988744,0.003295367,0.01061566,0.9458722,0.0002184867],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.0002305296,0.00002512096,0.0006578688,0.0001379165,0.0000357616,0.0001469613,0.997305,0.0009127026,0.0005480484],"genre_scores_gemma":[0.004591717,0.000110963,0.008107842,0.0004163158,0.0001041266,0.00184619,0.9799589,0.0009697095,0.00389431],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7538056,"threshold_uncertainty_score":0.3511664,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07103293312264586,"score_gpt":0.3839408609601263,"score_spread":0.3129079278374804,"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."}}