{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005984801,0.00007653044,0.0001290645,0.000005227951,0.0008945346,0.0000139096,0.0004637774,0.00002922709,0.00002539365],"category_scores_gemma":[0.0003770592,0.0000179777,0.00003323296,0.00005967279,0.0001939816,0.0001137031,0.0002354793,0.0001014039,0.000001044244],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00000923632,"about_ca_system_score_gemma":0.000009779192,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008564148,"about_ca_topic_score_gemma":0.0000739473,"domain_scores_codex":[0.9993286,0.00007138716,0.0002424562,0.0001055884,0.0001145288,0.0001374924],"domain_scores_gemma":[0.9987209,0.0007740912,0.0002216622,0.0001370555,0.0001050651,0.00004123735],"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":[0.002335471,0.0002904121,0.01805232,0.0002717702,0.00015484,0.000005075469,0.002214583,0.000003907697,0.7738378,0.005202715,0.001523627,0.1961075],"study_design_scores_gemma":[0.000303497,0.002034427,0.8717345,0.0006053072,0.00002504009,0.000019629,0.009683898,0.00008997893,0.002160525,0.00003209168,0.1132278,0.00008330389],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9977658,0.00006067608,0.000107014,0.001439064,0.0001335323,0.0003835522,0.00005558697,0.000007301208,0.00004747866],"genre_scores_gemma":[0.9991847,0.000009049179,0.0003906378,0.0000871351,0.0001655938,0.00005630215,0.00002332022,6.721983e-7,0.00008263237],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8536822,"threshold_uncertainty_score":0.6880127,"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."}}