{"id":"W6957745587","doi":"10.6068/dp14ba843872863","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: Pineapples, Niacin (niacin equivalent), Nutrients available | Units: Milligrams, 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; Economic statistics; Census; Official statistics; Greenhouse; Nutrient; Commodity; 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":[0.001896637,0.002415878,0.00262521,0.007681656,0.003085949,0.004433077,0.004904065,0.001466691,0.1060335],"category_scores_gemma":[0.01556577,0.001714384,0.002149069,0.0419215,0.000639764,0.002476093,0.002108882,0.002890259,0.05485466],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.05402094,"about_ca_system_score_gemma":0.1376969,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9948985,"about_ca_topic_score_gemma":0.9928422,"domain_scores_codex":[0.9963425,0.0002218841,0.000408452,0.0004913417,0.001742041,0.0007938115],"domain_scores_gemma":[0.9691147,0.001098878,0.0009111905,0.0008235961,0.02667753,0.001374271],"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":[0.00002815691,0.000006656603,0.001044366,0.0003428046,0.0000243714,0.000006878709,0.00002127332,0.000124176,0.00001290004,0.0004472262,0.9959834,0.001957926],"study_design_scores_gemma":[0.0001460913,0.00001222557,0.02170782,0.0008385427,0.00006816416,0.00002259311,0.0003607133,0.0003673071,0.0001710873,0.0006327643,0.9755951,0.00007752165],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00004544446,0.00005643096,0.00002207337,0.0001065978,0.00002540016,0.00001372798,0.9987144,0.00004755282,0.0009683915],"genre_scores_gemma":[0.0009353651,0.000427189,0.0004582665,0.0001747308,0.0000178116,0.0001341535,0.9926766,0.0001231963,0.005052586],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1060335,"threshold_uncertainty_score":0.3919512,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02405189342031379,"score_gpt":0.2320883128452416,"score_spread":0.2080364194249278,"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."}}