{"id":"W2791748821","doi":"10.1093/jas/skx073","title":"Estimating optimal observational sampling frequency of behaviors for cattle fed high- and low-forage diets","year":2018,"lang":"en","type":"article","venue":"Journal of Animal Science","topic":"Animal Behavior and Welfare Studies","field":"Veterinary","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"Agriculture and Agri-Food Canada","keywords":"Ruminating; Forage; Animal science; Crossbreed; Mathematics; Barn; Sampling (signal processing); Rumination; Grazing; Statistics; Biology; Agronomy","routes":{"ca_aff":true,"ca_fund":true,"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":[0.001812879,0.0003151402,0.0006538875,0.0006507284,0.0003392562,0.0005528606,0.000467202,0.000531908,0.0003137307],"category_scores_gemma":[0.005680216,0.000361685,0.0002554215,0.0004045937,0.0002922681,0.0004232185,0.0002805935,0.0002650948,0.0001155428],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005493729,"about_ca_system_score_gemma":0.0004958868,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005302547,"about_ca_topic_score_gemma":0.01656886,"domain_scores_codex":[0.9990873,0.0003489911,0.00008103147,0.0002114176,0.0001980029,0.00007337848],"domain_scores_gemma":[0.9968846,0.00148646,0.0008931154,0.0001646057,0.0004168841,0.000154351],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.003355689,0.000711815,0.7372776,0.0003668144,0.0002079578,0.00008989997,0.0007988925,0.003096391,0.1819507,0.00009720283,0.0002297064,0.07181717],"study_design_scores_gemma":[0.00003963355,0.001005596,0.9732673,0.00003171259,0.0001068058,0.0001295196,0.0002404909,0.01168832,0.01306201,0.00005253728,0.0003576087,0.00001848342],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.98365,0.0002624022,0.01558642,0.00001651469,0.000005188948,0.00006151925,0.0001102508,0.00003871562,0.000268962],"genre_scores_gemma":[0.9545242,0.0002698339,0.04433588,0.00003639174,0.00001939684,0.0001891057,0.0004083358,0.00001590553,0.0002009324],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005302547,"threshold_uncertainty_score":0.01054335,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1289481699469718,"score_gpt":0.3967096533049866,"score_spread":0.2677614833580149,"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."}}