{"id":"W3108303109","doi":"10.1093/jas/skaa278.231","title":"174 A comparison of machine learning algorithms in the classification of beef steers finished in feedlot","year":2020,"lang":"en","type":"article","venue":"Journal of Animal Science","topic":"Meat and Animal Product Quality","field":"Agricultural and Biological Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Feedlot; Artificial intelligence; Machine learning; Random forest; Decision tree; Naive Bayes classifier; Artificial neural network; Multilayer perceptron; Beef cattle; Computer science; Statistical classification; Algorithm; Classifier (UML); Mathematics; Support vector machine; Animal science; Biology","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":[0.004584734,0.0007777187,0.0008970761,0.002321089,0.0003563489,0.0009837633,0.0006915952,0.0009985385,0.001034799],"category_scores_gemma":[0.006048655,0.0001569478,0.0007843595,0.001049528,0.0002110204,0.0006916822,0.0003518836,0.0005168279,0.0006246592],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006976181,"about_ca_system_score_gemma":0.0007617144,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007073427,"about_ca_topic_score_gemma":0.002822126,"domain_scores_codex":[0.9978732,0.0009890869,0.0002626243,0.0002894074,0.0004077323,0.0001778874],"domain_scores_gemma":[0.9956286,0.002882018,0.0001958592,0.0001383317,0.001061398,0.00009373923],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002245222,0.0007837996,0.0657609,0.0003406557,0.0005078229,0.0001119211,0.0001265905,0.1923598,0.006452021,0.0006868982,0.002589711,0.7280346],"study_design_scores_gemma":[0.00003806877,0.0005709657,0.02413581,0.00005684285,0.00007609141,0.00006447333,0.0001256906,0.9695742,0.004255448,0.0004523278,0.0006245538,0.00002559392],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8211157,0.003417701,0.1672232,0.0003227151,0.0002783964,0.0002292006,0.0006539643,0.001766006,0.004993117],"genre_scores_gemma":[0.9277984,0.0004412134,0.06931224,0.00008908196,0.00003857086,0.00008896234,0.0009133488,0.00003863753,0.001279541],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007073427,"threshold_uncertainty_score":0.02424663,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1230069877314979,"score_gpt":0.3250483339087524,"score_spread":0.2020413461772545,"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."}}