{"id":"W6969196478","doi":"10.5683/sp3/4qvi0o","title":"Supplementary material for: Fitting mathematical functions to extended lactation curves and forecasting late-lactation milk yields of dairy cows","year":2023,"lang":"en","type":"dataset","venue":"Socio-Environmental Systems Modeling","topic":"Transport and Economic Policies","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"","keywords":"Code (set theory); Lactation; Mathematical model; Mathematical statistics; Dairy cattle; Source code","routes":{"ca_aff":true,"ca_fund":false,"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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005957758,0.0003459623,0.0005781881,0.0002349209,0.0003421493,0.0001546917,0.0001717275,0.0002243471,0.0001513656],"category_scores_gemma":[0.00002877612,0.0003705061,0.0001454341,0.00007605337,0.00005006388,0.0004753108,0.0001500071,0.000157289,0.00009433556],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001324958,"about_ca_system_score_gemma":0.00001372445,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003181482,"about_ca_topic_score_gemma":0.00008224364,"domain_scores_codex":[0.9978663,0.0000156194,0.001031186,0.0004346182,0.0002618304,0.0003903909],"domain_scores_gemma":[0.9991025,0.00009869032,0.0005370547,0.0002110638,0.00001784148,0.00003280826],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002144906,0.0001811282,0.004471905,0.0177002,0.0004118366,0.000004807004,0.0005634564,0.02642815,0.0008573846,0.0001216453,0.9486242,0.0004207988],"study_design_scores_gemma":[0.006489412,0.0003144486,0.002256279,0.01193717,0.004351301,0.00002489774,0.1304173,0.6031812,0.00002403015,0.003476617,0.2324148,0.005112531],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.2909877,0.00007201936,0.001793417,0.0001674651,0.001063695,0.001202386,0.7046381,0.00005408349,0.00002111863],"genre_scores_gemma":[0.218042,0.0000759528,0.0001316253,0.0001706359,0.001048565,0.0002700479,0.7801031,0.00006368413,0.00009439494],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.7162094,"threshold_uncertainty_score":0.9998747,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03618393550380329,"score_gpt":0.2316007492278052,"score_spread":0.1954168137240019,"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."}}