{"id":"W4392114789","doi":"","title":"To go or not to go? Assessing anticipation for outdoor access in dairy cows.","year":2023,"lang":"en","type":"article","venue":"HAL (Le Centre pour la Communication Scientifique Directe)","topic":"Animal Behavior and Welfare Studies","field":"Veterinary","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Anticipation (artificial intelligence); Dairy cattle; Computer science; Animal science; Artificial intelligence; 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":[0.0008532579,0.0002199672,0.0003069543,0.0003153933,0.0002526447,0.0007279877,0.0002248342,0.0006923361,0.002901827],"category_scores_gemma":[0.002723005,0.0001159889,0.0001970747,0.0001813335,0.0002126346,0.0002350408,0.0003348192,0.0004742759,0.000260272],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003289911,"about_ca_system_score_gemma":0.0003797178,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005055116,"about_ca_topic_score_gemma":0.01645885,"domain_scores_codex":[0.9998084,0.00007409352,0.000008693341,0.00003851958,0.00003226573,0.00003799515],"domain_scores_gemma":[0.998687,0.0004400993,0.0005450064,0.00002476855,0.00008260467,0.0002205251],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002484859,0.0005373672,0.9423493,0.0003766051,0.0001863889,0.0002133665,0.001966293,0.0003350742,0.009300129,0.000157389,0.001927142,0.04016604],"study_design_scores_gemma":[0.00000807976,0.0007262106,0.9962859,0.00006755094,0.000038153,0.00009849096,0.001398032,0.0003854632,0.0002852628,0.0001552162,0.0005407078,0.00001095078],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9968739,0.0007496401,0.0003445721,0.0001611418,0.00001669378,0.00001966672,0.0002575677,0.000009967143,0.001566825],"genre_scores_gemma":[0.9967584,0.0008740138,0.0008710726,0.0001383943,0.00002064972,0.00005424234,0.0002949151,0.000003882464,0.0009844211],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005055116,"threshold_uncertainty_score":0.01005143,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1270104822608873,"score_gpt":0.3999407173952211,"score_spread":0.2729302351343338,"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."}}