{"id":"W6920466794","doi":"10.6068/dp14ba8438c5664","title":"Trend 1976 - 2009. Statistics Canada. CANSIM: Agriculture - Crops and Horticulture | Country: Canada | Table: Nutrients in the food supply, by source of nutritional equivalent and commodity | Variable: Salad oils, Energy, Nutrients available adjusted for losses | Units: Kilocalories, 1976-2009. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. Dataset-ID: 075-001-002.","year":2015,"lang":"en","type":"other","venue":"Data Planet","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Agriculture; Census; Economic statistics; Official statistics; Greenhouse; Commodity; Nutrient; Statistical analysis","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001703631,0.002460224,0.00252882,0.007548516,0.00304571,0.004311489,0.004768546,0.001438715,0.1054819],"category_scores_gemma":[0.01390072,0.001611829,0.002119924,0.03939121,0.0006187145,0.002414599,0.002122555,0.002800638,0.05581776],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.05039707,"about_ca_system_score_gemma":0.1249457,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9946145,"about_ca_topic_score_gemma":0.9924856,"domain_scores_codex":[0.9966956,0.0002040932,0.0003531869,0.0004579594,0.001508263,0.0007808993],"domain_scores_gemma":[0.973899,0.000928256,0.0008466702,0.0007373643,0.02233032,0.001258304],"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":[0.0000264877,0.000006292868,0.001091046,0.00032178,0.00002425526,0.000007147475,0.00002099652,0.0001229397,0.00001279363,0.0004358182,0.9961486,0.001781786],"study_design_scores_gemma":[0.0001401989,0.00001148192,0.02243096,0.0007910638,0.000064912,0.00002493601,0.0003788895,0.0003827473,0.0001739239,0.0006041777,0.9749196,0.00007713924],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00004603477,0.00005383673,0.00002032043,0.00009623088,0.00002395735,0.00001192451,0.9988306,0.00004285686,0.0008742845],"genre_scores_gemma":[0.0008974337,0.0003675948,0.0003855988,0.0001507967,0.00001677689,0.0001146807,0.9934564,0.0001068974,0.004503795],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1054819,"threshold_uncertainty_score":0.365658,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02446124187646288,"score_gpt":0.2274379424868648,"score_spread":0.2029767006104019,"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."}}