{"id":"W6887752326","doi":"10.17632/fspgsfh2km","title":"Day-to-day variation in trace element and macro-mineral concentrations in corn and mixed grass-legume silages of Canadian commercial dairy herds - Raw dataset","year":2023,"lang":"en","type":"dataset","venue":"Mendeley Data","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Agriculture and Agri-Food Canada","funders":"","keywords":"Trace element; Raw milk; Herd; Raw material; Sampling (signal processing); Silage; Dairy cattle","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":[0.003731255,0.0006981306,0.00103378,0.002288825,0.0001815183,0.0002377061,0.001710234,0.0004266671,0.00009327517],"category_scores_gemma":[0.001026275,0.0008118585,0.00002663542,0.001988835,0.0002150447,0.0008091961,0.001378061,0.0007713893,0.0002636664],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005819185,"about_ca_system_score_gemma":0.00113798,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.6161734,"about_ca_topic_score_gemma":0.9920897,"domain_scores_codex":[0.9942516,0.001033335,0.001393833,0.00156146,0.0007155927,0.001044196],"domain_scores_gemma":[0.995425,0.0005409279,0.0004836521,0.002862233,0.00007734082,0.0006108074],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00007917478,0.0001896106,0.002551693,0.000186185,0.0001043592,0.0001701025,0.0002499622,0.00005403434,0.0006299926,0.00002886329,0.9954138,0.000342262],"study_design_scores_gemma":[0.002495332,0.000107959,0.153511,0.000315551,0.0002365362,0.00001192565,0.0002464384,0.001040842,0.00003501529,0.00002317174,0.8411756,0.0008006779],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.006811159,0.0001900153,0.00001402699,0.0008015558,0.0004664526,0.001674821,0.9900015,0.00003243519,0.000008036396],"genre_scores_gemma":[0.008584624,0.000622715,0.0002226661,0.0002023352,0.0001586181,0.0001299991,0.9899525,0.0001084296,0.00001811658],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.3759163,"threshold_uncertainty_score":0.9994332,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05515752407449269,"score_gpt":0.3129052874442519,"score_spread":0.2577477633697592,"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."}}