{"id":"W6939120921","doi":"10.6068/dp14ba8a1b39971","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: Apple juice, Vitamin A (retinol equivalent), Nutrients available adjusted for losses | 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; Nutrient","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":[0.001763856,0.002378068,0.002517327,0.008068511,0.002957939,0.004111319,0.004762653,0.001500296,0.1098676],"category_scores_gemma":[0.01445665,0.00155359,0.002244277,0.03902651,0.0006226894,0.002271672,0.001985613,0.002719115,0.05451784],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04882489,"about_ca_system_score_gemma":0.1259056,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.993648,"about_ca_topic_score_gemma":0.9916373,"domain_scores_codex":[0.9966601,0.0002040461,0.0003735535,0.0004706263,0.001564418,0.0007272438],"domain_scores_gemma":[0.9726799,0.001013281,0.0009056749,0.0007552619,0.02338827,0.001257532],"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":[0.00002612508,0.000006129659,0.001223656,0.0003619138,0.00002651458,0.000006828008,0.00001863862,0.0001208518,0.00001344311,0.0004209219,0.9958975,0.001877438],"study_design_scores_gemma":[0.0001400286,0.00001196759,0.02371739,0.0009126093,0.00007345819,0.00002718093,0.0003216797,0.0003557604,0.0001657362,0.0006427076,0.9735568,0.00007460154],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00004397182,0.00005851315,0.00002172731,0.00009407951,0.00002405971,0.00001363604,0.9988294,0.00004296591,0.0008716539],"genre_scores_gemma":[0.000923834,0.0004192979,0.0004420518,0.000171559,0.00001886851,0.0001429744,0.9929332,0.0001160524,0.004832205],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1098676,"threshold_uncertainty_score":0.3675436,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02889999216760567,"score_gpt":0.24007419424307,"score_spread":0.2111742020754644,"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."}}