{"id":"W7015639834","doi":"","title":"Tomorrow’s turkeys – breeding to improve turkey health and welfare","year":2024,"lang":"de","type":"article","venue":"","topic":"Animal Nutrition and Physiology","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Guelph","funders":"","keywords":"Welfare; Population; Animal health; Government (linguistics); Human welfare","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":[0.0004254172,0.0004876333,0.0002365195,0.0004027416,0.0008938002,0.0005235209,0.000432845,0.0003755325,0.005973981],"category_scores_gemma":[0.0001858159,0.0001441483,0.0002682845,0.0002697876,0.0002970522,0.0002535552,0.0003112173,0.00051959,0.00112164],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000912196,"about_ca_system_score_gemma":0.001085887,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01251415,"about_ca_topic_score_gemma":0.05969584,"domain_scores_codex":[0.9998406,0.00002661454,0.000007645844,0.00005375556,0.00002023306,0.00005108901],"domain_scores_gemma":[0.9997509,0.000007680629,0.00004713871,0.00002498087,0.00005873712,0.0001104617],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"not_applicable","study_design_scores_codex":[0.003750356,0.003105379,0.1206048,0.0003907258,0.0002312997,0.00214381,0.001730129,0.0004861284,0.5832115,0.002601145,0.02093543,0.2608093],"study_design_scores_gemma":[0.0002505102,0.008885625,0.8497617,0.0002233535,0.0002698511,0.002777264,0.006821494,0.000792177,0.04758298,0.000700129,0.08186602,0.0000688471],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9844135,0.0004640431,0.000789371,0.0007283405,0.0002921475,0.00008411289,0.0003603038,0.0001445903,0.01272345],"genre_scores_gemma":[0.9571257,0.0008136932,0.01144289,0.0007910146,0.00006408114,0.00008934997,0.00124345,0.00007705373,0.0283526],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01251415,"threshold_uncertainty_score":0.02488261,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02107686856815073,"score_gpt":0.2593748153576363,"score_spread":0.2382979467894856,"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."}}