{"id":"W6957743475","doi":"10.6068/dp14ba81e0f7498","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: Fish, total, Carbohydrates, Nutrients available | Units: Milligrams Grams, 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":"Historical Astronomy and Related Studies","field":"Physics and Astronomy","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Agriculture; Census; Economic statistics; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.000229941,0.0006609456,0.0009186776,0.00004276498,0.0002852376,0.0001579779,0.0009731343,0.0003344082,0.0006402911],"category_scores_gemma":[0.00001649529,0.0004740742,4.640201e-7,0.0003688989,0.0003012142,0.0001445222,0.0004496947,0.0009687685,0.000004095096],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001457927,"about_ca_system_score_gemma":0.001981251,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9982642,"about_ca_topic_score_gemma":0.9784464,"domain_scores_codex":[0.9965999,0.0002881014,0.0007282613,0.0008590446,0.000840328,0.0006843632],"domain_scores_gemma":[0.997696,0.0003286869,0.0005169099,0.0009920446,0.00009107697,0.0003753133],"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":[0.00004641824,0.0003757677,0.0002307746,0.0002553347,0.0004610508,0.00003338403,0.000009630595,0.00001468133,0.000001391744,0.0003210742,0.9982065,0.00004398976],"study_design_scores_gemma":[0.001222691,0.0001225602,0.00000592129,0.00007635071,0.0004078218,0.00002032912,0.0004856052,0.0001900245,9.479976e-8,9.637121e-7,0.9969006,0.0005670071],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.000005375011,0.008956715,0.000009100389,0.000009659345,0.0002837814,0.0006479928,0.9887023,0.00001554135,0.001369521],"genre_scores_gemma":[0.0001975246,0.0008175533,0.00004736689,0.0000817391,0.0001951589,0.00003585975,0.995443,0.00006133207,0.003120464],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.01981779,"threshold_uncertainty_score":0.9997711,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01287473536042951,"score_gpt":0.2028544800389105,"score_spread":0.1899797446784809,"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."}}