{"id":"W6939176257","doi":"10.6068/dp14ba7fc187e95","title":"Trend 1960 - 2012. Statistics Canada. CANSIM: Manufacturing - Food, Beverage and Tobacco | Country: Canada | Table: Supply and disposition of food in Canada | Variable: Powder skim milk, Production | Units: Tonnes x 1,000, 1960-2012. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. Dataset-ID: 075-001-151.","year":2015,"lang":"en","type":"other","venue":"Data Planet","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Economic statistics; Production (economics); Statistics education; Statistical analysis; Census; Descriptive statistics; Food processing; Food supply; Official statistics","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","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0007067269,0.001223449,0.001565353,0.0002709719,0.0001948201,0.0002553748,0.001518581,0.0004733677,0.001819502],"category_scores_gemma":[0.0001402577,0.001259381,1.807766e-7,0.0004674526,0.0002673444,0.0009109148,0.0007913446,0.001038953,0.000007965141],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00142527,"about_ca_system_score_gemma":0.02990267,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9999971,"about_ca_topic_score_gemma":0.9999994,"domain_scores_codex":[0.9929832,0.0005925696,0.001386769,0.001960605,0.001787475,0.001289335],"domain_scores_gemma":[0.9942701,0.000568022,0.001139359,0.003067337,0.000126634,0.0008285743],"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":[0.0002436127,0.00007907188,0.0001095103,0.001247013,0.0004991041,0.0004159155,0.000006532819,0.00009799792,0.00001425675,0.0001465641,0.9969558,0.0001846141],"study_design_scores_gemma":[0.001343383,0.000156348,0.00006874276,0.000168075,0.0005471593,0.0003932931,0.0003054643,0.0003275506,0.000001395588,5.438441e-7,0.9953369,0.001351111],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00003529643,0.00521281,0.000002478334,0.000005558921,0.00111385,0.001262076,0.9916447,0.00005466746,0.000668565],"genre_scores_gemma":[0.0006201311,0.0006597178,0.0001367371,0.000137153,0.0002881693,0.00003432994,0.9961822,0.0004969092,0.001444643],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.0284774,"threshold_uncertainty_score":0.999093,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01571081993590605,"score_gpt":0.2163667886423206,"score_spread":0.2006559687064145,"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."}}