{"id":"W6920249806","doi":"10.6068/dp14ba82328ad78","title":"Trend 1976 - 2009. Statistics Canada. CANSIM: Agriculture - Livestock and Aquaculture | Country: Canada | Table: Nutrients in the food supply, by source of nutritional equivalent and commodity | Variable: Cheeses, total, Magnesium, Nutrients available | Units: Milligrams, 1976-2009. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. Dataset-ID: 075-001-007.","year":2015,"lang":"en","type":"other","venue":"Data Planet","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Livestock; Agriculture; Economic statistics; Census; Commodity; Official statistics; Statistical analysis; Socioeconomic status","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":[0.001802827,0.00238034,0.002615278,0.008306252,0.00304076,0.004440057,0.004845764,0.001455344,0.09723994],"category_scores_gemma":[0.01507584,0.001631763,0.002101833,0.04117041,0.0006770426,0.002437276,0.002139246,0.002868161,0.05299066],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.05705313,"about_ca_system_score_gemma":0.1436369,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9954486,"about_ca_topic_score_gemma":0.9937884,"domain_scores_codex":[0.9962788,0.0002224428,0.0004033255,0.0004809942,0.001785596,0.0008288102],"domain_scores_gemma":[0.9706364,0.001010594,0.0009249831,0.0007419552,0.02528499,0.001401133],"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":[0.00002479857,0.00000635336,0.001114415,0.0002981687,0.00002313907,0.00000687853,0.00002022337,0.0001124689,0.00001190551,0.0004034681,0.9962804,0.001697844],"study_design_scores_gemma":[0.0001424861,0.00001217905,0.02475323,0.0008835825,0.00007209269,0.00002620176,0.0004340597,0.0004019516,0.0001809716,0.0006289941,0.9723818,0.00008233754],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00004854404,0.00005692976,0.00002125065,0.0001154845,0.00002608809,0.0000136662,0.9987608,0.00004455787,0.0009126874],"genre_scores_gemma":[0.001014668,0.0004366097,0.0004402401,0.0001844473,0.00001932533,0.0001307032,0.9924695,0.000111122,0.005193427],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.09723994,"threshold_uncertainty_score":0.4139513,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02290588088419068,"score_gpt":0.2289438307728657,"score_spread":0.206037949888675,"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."}}