{"id":"W2048433325","doi":"10.1016/j.prevetmed.2008.02.013","title":"Linking causal concepts, study design, analysis and inference in support of one epidemiology for population health","year":2008,"lang":"en","type":"article","venue":"Preventive Veterinary Medicine","topic":"Advanced Causal Inference Techniques","field":"Mathematics","cited_by":16,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Observational study; Causal inference; Causation; Interpretation (philosophy); Inference; Management science; Research design; Population; Causality (physics); Observational methods in psychology; Computer science; Data science; Psychology; Medicine; Epistemology; Engineering; Environmental health; Mathematics; Statistics; Artificial intelligence; Pathology","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":[0.003033079,0.0001692546,0.001092833,0.0003938977,0.00006908751,8.994263e-7,0.0001087158,0.00006793613,0.00003622266],"category_scores_gemma":[0.002904842,0.0001499975,0.000050022,0.0004266697,0.0002115557,0.0001186161,0.00007498249,0.0001413064,1.713445e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007502503,"about_ca_system_score_gemma":0.00004705203,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004562112,"about_ca_topic_score_gemma":0.0001172795,"domain_scores_codex":[0.9974181,0.0008829738,0.000969681,0.0003294142,0.0001446353,0.0002551638],"domain_scores_gemma":[0.9947141,0.004269394,0.0005929924,0.0002425193,0.0001102078,0.00007075927],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004037875,0.0007981795,0.9764044,0.0004738107,0.0004251601,0.0000395487,0.008520952,0.00006635386,0.002560682,0.003668221,0.00006660503,0.006572248],"study_design_scores_gemma":[0.002286754,0.02299546,0.7624825,0.000493176,0.0004400297,0.00003693602,0.0007859019,0.001103642,0.0003800435,0.2086326,0.00003735709,0.0003255683],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5236807,0.0001243534,0.4750228,0.0001398386,0.0000176154,0.000967399,0.000005878884,0.00003529698,0.000006097755],"genre_scores_gemma":[0.8796507,0.0001860319,0.1197925,0.0001009325,0.00002693031,0.0001573729,0.00004346512,0.00001278481,0.00002934492],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3559699,"threshold_uncertainty_score":0.6116721,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.464850136985362,"score_gpt":0.5435031123947849,"score_spread":0.07865297540942284,"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."}}