{"id":"W4307156845","doi":"10.3389/fvets.2022.1004801","title":"Watch your language: An exploration of the use of causal wording in veterinary observational research","year":2022,"lang":"en","type":"article","venue":"Frontiers in Veterinary Science","topic":"Health Policy Implementation Science","field":"Health Professions","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"University of Guelph","keywords":"Observational study; Causal inference; Confounding; Outcome (game theory); Causality (physics); Causation; Psychology; Causal model; Categorization; Medicine; Computer science; Pathology; Epistemology; Artificial intelligence; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":["metaresearch"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.2793252,0.001247778,0.002509752,0.01630961,0.003682405,0.01184013,0.003304912,0.005513997,0.01615346],"category_scores_gemma":[0.7403257,0.001856581,0.004589573,0.0311446,0.009661621,0.02053846,0.0103568,0.006044138,0.00135364],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005714525,"about_ca_system_score_gemma":0.009957513,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007297874,"about_ca_topic_score_gemma":0.01227994,"domain_scores_codex":[0.6253986,0.3108499,0.03396636,0.01009333,0.01792509,0.001766725],"domain_scores_gemma":[0.06391306,0.8874348,0.03008302,0.01039492,0.007532444,0.0006416985],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.003068092,0.0001766023,0.07923888,0.1209737,0.005930945,0.00136171,0.1546666,0.003286098,0.002716846,0.3061951,0.08967113,0.2327142],"study_design_scores_gemma":[0.001454822,0.000770998,0.03779095,0.1426145,0.006156588,0.001949135,0.05153182,0.01169274,0.001687203,0.3024187,0.44115,0.0007825888],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1043319,0.2137138,0.3783529,0.2114083,0.0121742,0.006435622,0.03706528,0.001685826,0.03483219],"genre_scores_gemma":[0.5203902,0.04414202,0.3400351,0.06085628,0.003746249,0.01832059,0.007624396,0.001548129,0.003336961],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7206748,"threshold_uncertainty_score":0.8887203,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.9267855268308053,"score_gpt":0.6933300632363124,"score_spread":0.2334554635944929,"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."}}