{"id":"W6930351276","doi":"10.5281/zenodo.14713756","title":"Canada's Fruit and Vegetable Flows","year":2025,"lang":"en","type":"dataset","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Skin and Cellular Biology Research","field":"Biochemistry, Genetics and Molecular Biology","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"State (computer science); Flow (mathematics); Agriculture; Production (economics); Yield (engineering)","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005565,0.001557686,0.001172432,0.00474251,0.002052401,0.002610248,0.002831511,0.001213701,0.03451191],"category_scores_gemma":[0.005070725,0.0006221698,0.001225197,0.0154156,0.0004870363,0.0007860445,0.001202687,0.001687161,0.0161503],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01737969,"about_ca_system_score_gemma":0.03115227,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9726194,"about_ca_topic_score_gemma":0.9839208,"domain_scores_codex":[0.9991258,0.00006297321,0.00005529643,0.0002083608,0.000322072,0.0002255749],"domain_scores_gemma":[0.9970436,0.0003332657,0.000158643,0.0002279118,0.001866694,0.0003699371],"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":[0.00004806664,0.0000122388,0.002690228,0.0002688023,0.00003980846,0.00002452126,0.00003357443,0.0003740337,0.00003476785,0.0007654045,0.9932827,0.002425823],"study_design_scores_gemma":[0.000139444,0.000009029826,0.02615028,0.0004323791,0.00006598753,0.0000627986,0.0002095935,0.001076032,0.0002866917,0.0008430164,0.9706693,0.00005545854],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0002437374,0.00008478652,0.00001774485,0.00007809194,0.000007816903,0.000004800035,0.9987381,0.000062803,0.0007621187],"genre_scores_gemma":[0.001372182,0.0001420455,0.0001909211,0.00006179468,0.00000387204,0.00002775325,0.9967477,0.00002770858,0.001425962],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.03451191,"threshold_uncertainty_score":0.1260991,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01210862235254664,"score_gpt":0.2293442868316949,"score_spread":0.2172356644791482,"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."}}