{"id":"W2409883685","doi":"10.1016/j.pmedr.2016.06.003","title":"Identifying and mitigating risks for agricultural injury associated with obesity","year":2016,"lang":"en","type":"article","venue":"Preventive Medicine Reports","topic":"Agriculture and Farm Safety","field":"Agricultural and Biological Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan; Queen's University","funders":"Canadian Institutes of Health Research; Canada Research Chairs","keywords":"Overweight; Obesity; Occupational injury; Body mass index; Medicine; Environmental health; Logistic regression; Odds ratio; Odds; Occupational safety and health; Demography; Injury prevention; Gerontology; Poison control; Internal medicine","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005460268,0.0001801739,0.0002830978,0.000008555479,0.0002687201,0.00001763307,0.00006696503,0.00008157752,0.00005473715],"category_scores_gemma":[0.0002297934,0.00004015982,0.00006775087,0.0001886747,0.0001338588,0.0001781087,0.00005337518,0.00007705412,9.743143e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003814302,"about_ca_system_score_gemma":0.000005578382,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009306429,"about_ca_topic_score_gemma":0.0002401679,"domain_scores_codex":[0.9986782,0.00007391248,0.0003318132,0.0003945639,0.0002464206,0.0002751088],"domain_scores_gemma":[0.9988824,0.0003496231,0.0003942045,0.00004641244,0.0002054791,0.000121946],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00005264351,0.0001279041,0.4124786,0.00002571396,0.0001838123,0.00006170743,0.0004713024,1.244174e-7,0.473097,0.000320626,0.001356463,0.1118241],"study_design_scores_gemma":[0.000245219,0.0004006471,0.9928819,0.0004615706,0.00009219395,0.0000805742,0.0005934855,7.261889e-7,0.002718065,0.001537639,0.0007996169,0.0001883547],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9972401,0.000236404,0.0002805612,0.00102465,0.0001207109,0.0005074358,0.00002067318,0.00007430543,0.0004951931],"genre_scores_gemma":[0.9982811,0.00004106791,0.0000608496,0.00005636517,0.0003468991,0.0000535666,0.00006847014,0.000001278371,0.001090379],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5804033,"threshold_uncertainty_score":0.2066805,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03285546099953144,"score_gpt":0.2736822154451988,"score_spread":0.2408267544456674,"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."}}