{"id":"W2250597415","doi":"10.63317/5k96z93j8umc","title":"The Meta-knowledge of Causality in Biomedical Scientific Discourse","year":2014,"lang":"en","type":"article","venue":"","topic":"Biomedical Text Mining and Ontologies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"Open Text (Canada)","funders":"Engineering and Physical Sciences Research Council; Medical Research Council","keywords":"Causality (physics); Computer science; Context (archaeology); Natural language processing; Workload; Artificial intelligence; Data science; Information retrieval; Biology","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":[],"consensus_categories":[],"category_scores_codex":[0.02580501,0.001052189,0.001650229,0.02064711,0.003050147,0.01386143,0.002930952,0.003412928,0.004731752],"category_scores_gemma":[0.09003053,0.001973116,0.002613035,0.01409691,0.009205137,0.04702646,0.006507564,0.004198369,0.0006478655],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003158388,"about_ca_system_score_gemma":0.00460364,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003218012,"about_ca_topic_score_gemma":0.002457386,"domain_scores_codex":[0.9736475,0.01297532,0.004720345,0.003797739,0.00439277,0.0004662994],"domain_scores_gemma":[0.8623863,0.1139118,0.00893305,0.008902128,0.004684763,0.001181943],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.000159067,0.00006717425,0.008833406,0.0014442,0.0004322919,0.0009165713,0.006378146,0.003154573,0.00144719,0.9161795,0.001960732,0.0590272],"study_design_scores_gemma":[0.0000229529,0.00001669418,0.001677105,0.0006299676,0.0003097398,0.0005143487,0.001134665,0.007018852,0.0009795644,0.977163,0.01049529,0.00003778178],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08461027,0.02338717,0.8204762,0.03667357,0.001006172,0.0002582076,0.004240124,0.0007546454,0.02859366],"genre_scores_gemma":[0.7689517,0.009051866,0.2120408,0.002305751,0.001318655,0.0003837797,0.003222048,0.0002002236,0.002525175],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02580501,"threshold_uncertainty_score":0.1364716,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03575341318553831,"score_gpt":0.339191547659026,"score_spread":0.3034381344734877,"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."}}