{"id":"W2737105619","doi":"10.1016/j.prevetmed.2017.07.012","title":"The use of large databases to inform the development of an intestinal scoring system for the poultry industry","year":2017,"lang":"en","type":"article","venue":"Preventive Veterinary Medicine","topic":"Animal Nutrition and Physiology","field":"Agricultural and Biological Sciences","cited_by":12,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Guelph","funders":"Elanco Animal Health","keywords":"Flock; Index (typography); Production (economics); Logistic regression; Poultry farming; Population; Broiler; Veterinary medicine; Statistics; Environmental health; Medicine; Computer science; Animal science; Mathematics; Biology; Economics","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":[0.03203792,0.0009323509,0.00158531,0.0143549,0.001054955,0.005122868,0.002443705,0.001548546,0.002218746],"category_scores_gemma":[0.112688,0.0005456733,0.001188265,0.01097625,0.0003594994,0.004267256,0.002982017,0.001675401,0.0009164949],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00183809,"about_ca_system_score_gemma":0.005886164,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02134473,"about_ca_topic_score_gemma":0.02785311,"domain_scores_codex":[0.9837195,0.007109039,0.004160834,0.001904097,0.00268809,0.0004184048],"domain_scores_gemma":[0.8667014,0.07967622,0.01405272,0.01371673,0.02350035,0.002352579],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.001198529,0.001246276,0.4102839,0.00400991,0.001760578,0.000890164,0.00116263,0.01359238,0.007308835,0.008398706,0.03732974,0.5128184],"study_design_scores_gemma":[0.0006080522,0.001159855,0.5076997,0.008207586,0.004891909,0.0021903,0.005444308,0.2085268,0.02392442,0.02957792,0.20726,0.0005092263],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.3820783,0.01226166,0.31371,0.01218032,0.0006835228,0.004051655,0.2551829,0.004215313,0.01563629],"genre_scores_gemma":[0.4621746,0.003144686,0.3344234,0.001410824,0.000182448,0.001776468,0.1959266,0.0002048072,0.0007562413],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03203792,"threshold_uncertainty_score":0.1694348,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2897938133082337,"score_gpt":0.3822046692434354,"score_spread":0.09241085593520171,"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."}}