{"id":"W2983654633","doi":"10.1016/j.prevetmed.2019.104824","title":"An animal health example of managing and analyzing a large volume of data on a PC: Modeling body weight and age of over 13 million cats for explanatory and predictive purposes","year":2019,"lang":"en","type":"article","venue":"Preventive Veterinary Medicine","topic":"Human-Animal Interaction Studies","field":"Biochemistry, Genetics and Molecular Biology","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Guelph","funders":"Ontario Veterinary College, University of Guelph; University of Guelph","keywords":"Descriptive statistics; Computer science; Linear model; Predictive modelling; Linear regression; Regression analysis; Statistics; Medicine; Data mining; Machine learning; Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004239382,0.0001200676,0.0003424079,0.0001024645,0.00004252232,0.000002692381,0.00008599474,0.00003790921,0.000009525109],"category_scores_gemma":[0.00003800279,0.0001053574,0.00002033009,0.00003764793,0.0001065476,0.00002862803,0.0002244597,0.00005030087,4.554462e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00000772531,"about_ca_system_score_gemma":0.00001387736,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001990437,"about_ca_topic_score_gemma":0.00002560947,"domain_scores_codex":[0.9990278,0.00009078876,0.0002881819,0.0003718535,0.000094279,0.0001271321],"domain_scores_gemma":[0.9993799,0.00004234325,0.0002085625,0.0002482838,0.00007086486,0.00005002089],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.00202812,0.0001506636,0.0184906,0.0009325463,0.0003412129,0.00000385708,0.002104235,0.000008785334,0.9749771,0.00005138265,0.0001452782,0.0007662296],"study_design_scores_gemma":[0.01780105,0.1500699,0.5116656,0.00649953,0.001006149,0.0001783188,0.01953007,0.2328657,0.04788293,0.0005988959,0.01054669,0.00135515],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9911721,0.00462375,0.003468855,0.00003907435,0.00003693034,0.0003721397,0.0002535482,0.000002993167,0.00003063798],"genre_scores_gemma":[0.9979727,0.001235584,0.0003948134,0.00003102583,0.00004792174,0.00001150366,0.0002610076,0.00001277014,0.00003263713],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9270942,"threshold_uncertainty_score":0.4296352,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06179904178016046,"score_gpt":0.3931133907374352,"score_spread":0.3313143489572748,"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."}}