{"id":"W6920369140","doi":"10.6068/dp14ba82ef3968","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: Bananas, Fibre, total dietary, Nutrients available | Units: Milligrams Grams, 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":[0.001892149,0.002437312,0.00268341,0.00816406,0.003251253,0.004582885,0.004892035,0.001469731,0.1036644],"category_scores_gemma":[0.01568211,0.001668169,0.002238665,0.04077509,0.0006898097,0.00253565,0.002205339,0.002957802,0.05757823],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.05720825,"about_ca_system_score_gemma":0.1452929,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9955655,"about_ca_topic_score_gemma":0.9938874,"domain_scores_codex":[0.9961566,0.0002330159,0.0004091169,0.000515349,0.001842738,0.0008431632],"domain_scores_gemma":[0.9692765,0.001065608,0.0009535737,0.0008152529,0.02648235,0.001406633],"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":[0.00002525458,0.000006499845,0.001165905,0.0003001,0.00002378981,0.000006780953,0.00002126398,0.0001122678,0.00001176639,0.000404248,0.996169,0.00175312],"study_design_scores_gemma":[0.0001414798,0.0000120544,0.0243986,0.0008528182,0.00007072403,0.00002549066,0.0004363207,0.0004014891,0.0001773817,0.0006226188,0.9727748,0.0000861216],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00004844922,0.00005681811,0.00002186954,0.0001104684,0.000026948,0.00001370671,0.9987547,0.00004589455,0.0009209742],"genre_scores_gemma":[0.0009754793,0.0004206125,0.0004494183,0.0001790244,0.00001936248,0.0001345709,0.9924085,0.0001171481,0.005295856],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1036644,"threshold_uncertainty_score":0.4150769,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02455229606593805,"score_gpt":0.2283700471625478,"score_spread":0.2038177510966098,"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."}}