{"id":"W6976301449","doi":"10.6068/dp14ba805b59389","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: Wines, Potassium, 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; Commodity; Greenhouse; 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":[0.001743851,0.002317735,0.002609856,0.007907531,0.0031662,0.004475982,0.004781343,0.001383328,0.0995843],"category_scores_gemma":[0.01484857,0.00160644,0.002073869,0.04196117,0.0006654648,0.002467152,0.002124707,0.002808267,0.05289676],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.05797699,"about_ca_system_score_gemma":0.1436,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9955484,"about_ca_topic_score_gemma":0.9937834,"domain_scores_codex":[0.9962983,0.0002199424,0.0003935996,0.0004775663,0.001760406,0.0008502733],"domain_scores_gemma":[0.9694535,0.001020624,0.0009195855,0.000744687,0.02647485,0.001386857],"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":[0.00002656277,0.000006302655,0.00112024,0.0003198087,0.00002309614,0.000007116234,0.00002184627,0.0001152923,0.00001211439,0.0004456227,0.9961084,0.001793669],"study_design_scores_gemma":[0.0001379731,0.00001200374,0.02404105,0.0008309347,0.00006945699,0.00002525438,0.0004399259,0.0003694331,0.0001842769,0.0006071609,0.9732036,0.00007900054],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00005147381,0.00005985828,0.00002160509,0.0001160475,0.00002704028,0.00001386374,0.9986448,0.00004513443,0.001020242],"genre_scores_gemma":[0.001081154,0.0004565781,0.0004389173,0.000181551,0.00001962307,0.0001311126,0.9918848,0.0001172094,0.005689031],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.0995843,"threshold_uncertainty_score":0.4206545,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02051733328320651,"score_gpt":0.225495284588039,"score_spread":0.2049779513048325,"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."}}