{"id":"W6901776520","doi":"10.6068/dp14ba82f04f415","title":"Trend 1989 - 2009. Statistics Canada. CANSIM: Agriculture - Food and Nutrition | Country: Canada | Table: Nutrients in the food supply, by source of nutritional equivalent and commodity | Variable: Kiwis, Fatty acids, mono-unsaturated, Nutrients available | Units: Milligrams Grams, 1989-2009. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. Dataset-ID: 075-001-005.","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; Official statistics; Census; Commodity; Food security; Food supply; Summary statistics; Food systems","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":[0.002000148,0.002364291,0.00270509,0.007604056,0.003118616,0.004236583,0.005040933,0.001464537,0.1102678],"category_scores_gemma":[0.01562546,0.001629251,0.002265864,0.04205701,0.0006340584,0.002437246,0.002220608,0.002928682,0.06319867],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.04943942,"about_ca_system_score_gemma":0.1223239,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.993455,"about_ca_topic_score_gemma":0.9907584,"domain_scores_codex":[0.9963504,0.0002560132,0.0004238357,0.0005112358,0.001661591,0.0007970562],"domain_scores_gemma":[0.9712205,0.00102011,0.0008681227,0.0008723603,0.02481149,0.001207553],"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":[0.00002953075,0.000006400538,0.001009215,0.0003317463,0.0000251466,0.000006727804,0.0000196662,0.00009328342,0.00001174376,0.0003739299,0.9962781,0.001814416],"study_design_scores_gemma":[0.0001548378,0.00001185382,0.02379476,0.0008867785,0.00007683908,0.00002580674,0.0003683464,0.0003413896,0.0001662141,0.0006343398,0.9734609,0.00007790085],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00003904735,0.00005186414,0.00002046024,0.00009604809,0.00002512309,0.00001286462,0.9989262,0.00004309422,0.0007852787],"genre_scores_gemma":[0.0007815891,0.0003613254,0.0003915162,0.0001597944,0.00001785751,0.000129928,0.9940499,0.0001032857,0.004004819],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1102678,"threshold_uncertainty_score":0.3688827,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02324678232663513,"score_gpt":0.22701414261224,"score_spread":0.2037673602856049,"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."}}