{"id":"W4297985373","doi":"10.1016/j.animal.2022.100641","title":"Using the Herd Status Index to remotely assess the welfare status of dairy herds based on prerecorded data","year":2022,"lang":"en","type":"article","venue":"animal","topic":"Animal Behavior and Welfare Studies","field":"Veterinary","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Agriculture and Agri-Food Canada; Dairy Farmers of Canada; Novalait; Ministère de l'Agriculture, des Pêcheries et de l'Alimentation; Natural Sciences and Engineering Research Council of Canada; McGill University","keywords":"Herd; Welfare; Cluster (spacecraft); Index (typography); Animal welfare; Statistics; Environmental health; Geography; Socioeconomics; Veterinary medicine; Mathematics; Medicine; Computer science; Biology; Economics; Ecology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004243157,0.0002460183,0.0002814792,0.00007291013,0.001120578,0.00005157461,0.0009583734,0.00004506129,0.000755746],"category_scores_gemma":[0.0001299106,0.0001625157,0.00009535116,0.0004170785,0.0001156092,0.0001283549,0.001732867,0.0004685597,0.000008834619],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000164988,"about_ca_system_score_gemma":0.0001594453,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003313133,"about_ca_topic_score_gemma":0.0001513227,"domain_scores_codex":[0.9974447,0.0004196773,0.0003461176,0.00050379,0.0006486389,0.0006370661],"domain_scores_gemma":[0.998199,0.0002017349,0.0001614587,0.001245456,0.00009239108,0.00009994955],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.02756606,0.001701852,0.8479711,0.0001945012,0.0005226794,0.0007176138,0.007404661,0.002262489,0.05057769,0.002298024,0.02376231,0.03502101],"study_design_scores_gemma":[0.0004905626,0.002635918,0.90967,0.00001702044,0.00008941689,0.00002322126,0.008173619,0.004391485,0.00005783334,0.00001174854,0.07418762,0.0002515397],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9927797,0.0001465241,0.0001798917,0.001521462,0.0002668732,0.0005584621,0.001390613,0.0000671414,0.003089365],"genre_scores_gemma":[0.9988302,0.000008049456,0.0003703724,0.0004736904,0.00009161839,0.00003451521,0.00008869131,0.000050793,0.00005205333],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06169891,"threshold_uncertainty_score":0.8618693,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2531577122062356,"score_gpt":0.411309631648567,"score_spread":0.1581519194423314,"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."}}