{"id":"W4402533514","doi":"10.1093/jas/skae234.050","title":"342 Sow weight development: A pragmatic approach to crossbred and selection populations","year":2024,"lang":"en","type":"article","venue":"Journal of Animal Science","topic":"Animal Behavior and Welfare Studies","field":"Veterinary","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"","keywords":"Crossbreed; Selection (genetic algorithm); Biology; Animal science; Computer science; Artificial intelligence","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":[0.02484659,0.0006033481,0.001074505,0.00111167,0.001097933,0.001529895,0.001629512,0.000785673,0.003233178],"category_scores_gemma":[0.02862975,0.0004509191,0.0005959433,0.0009196238,0.0008102503,0.0007432381,0.002555237,0.001144558,0.0004737658],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008547776,"about_ca_system_score_gemma":0.001503214,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00403185,"about_ca_topic_score_gemma":0.01012332,"domain_scores_codex":[0.9836802,0.01332118,0.0004162578,0.001047219,0.001353652,0.0001813042],"domain_scores_gemma":[0.9923428,0.003890247,0.0008510501,0.00140649,0.001191067,0.0003184035],"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":[0.0009643593,0.001366379,0.2794724,0.001157406,0.001578115,0.003432275,0.01047983,0.01307902,0.02863574,0.0679106,0.008155528,0.5837684],"study_design_scores_gemma":[0.0006874038,0.006198408,0.561347,0.001321462,0.001796313,0.003762169,0.01147008,0.2210655,0.00990046,0.09546031,0.08666626,0.0003244809],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4017995,0.0008018548,0.5741482,0.001870152,0.0002046629,0.003530908,0.0005699553,0.0003259742,0.01674887],"genre_scores_gemma":[0.6050078,0.0004176188,0.3825388,0.00132723,0.0001176313,0.004498939,0.000385358,0.0001193321,0.005587302],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02484659,"threshold_uncertainty_score":0.131403,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07974824710233741,"score_gpt":0.3730501906310059,"score_spread":0.2933019435286685,"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."}}