{"id":"W1993725192","doi":"10.1080/10888700902719781","title":"Using Data Collected for Production or Economic Purposes to Research Production Animal Welfare: An Epidemiological Approach","year":2009,"lang":"en","type":"article","venue":"Journal of Applied Animal Welfare Science","topic":"Animal Disease Management and Epidemiology","field":"Agricultural and Biological Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"University of Cambridge","keywords":"Confounding; Production (economics); Animal welfare; Outcome (game theory); Welfare; Negative binomial distribution; Epidemiology; Distribution (mathematics); Census; Econometrics; Statistics; Environmental health; Operations research; Computer science; Medicine; Mathematics; Economics; Population; Biology; Ecology","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":[0.05682652,0.001196599,0.002315582,0.01636988,0.001543605,0.003149665,0.002119613,0.001796557,0.001409141],"category_scores_gemma":[0.1193035,0.0009759175,0.00135556,0.01897654,0.001875487,0.004898516,0.002836812,0.002438633,0.0003310005],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002168487,"about_ca_system_score_gemma":0.003289582,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006754625,"about_ca_topic_score_gemma":0.007161443,"domain_scores_codex":[0.9100332,0.07337201,0.00777556,0.003186827,0.00512382,0.0005086015],"domain_scores_gemma":[0.8336822,0.1226875,0.02009373,0.01267877,0.00979919,0.001058621],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0007334629,0.001980048,0.722429,0.008548722,0.006697461,0.0006971121,0.005961496,0.007294266,0.002926611,0.04684207,0.004884913,0.1910049],"study_design_scores_gemma":[0.0005131625,0.004222452,0.7384602,0.00927274,0.004244419,0.002193824,0.01804645,0.04423634,0.007234453,0.100805,0.06999049,0.0007804421],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3367757,0.01679417,0.5895383,0.009433876,0.001199199,0.008358723,0.01802682,0.0002576805,0.01961545],"genre_scores_gemma":[0.6604453,0.007933045,0.3147494,0.002699676,0.0004167373,0.007241766,0.005069463,0.00006679124,0.001377795],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.05682652,"threshold_uncertainty_score":0.3005309,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2857300601907761,"score_gpt":0.4042250193572786,"score_spread":0.1184949591665025,"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."}}