{"id":"W6958164149","doi":"10.6068/dp14ba817622d53","title":"Trend 1961 - 2012. Statistics Canada. CANSIM: Manufacturing - Food, Beverage and Tobacco | Country: Canada | Table: Supply and disposition of food in Canada | Variable: Partly skimmed milk 2%, Total disposition | Units: (kilolitres), 1961-2012. 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; Official statistics; Descriptive statistics; Food supply; Census; Statistical analysis; Comparability; Summary statistics; Statistics education","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":[0.002071766,0.002357014,0.002687089,0.008907615,0.003242365,0.004846175,0.004936873,0.001540142,0.09494682],"category_scores_gemma":[0.01955771,0.001830492,0.0021648,0.04466652,0.0006922669,0.002674368,0.002207937,0.003223632,0.05841795],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0574287,"about_ca_system_score_gemma":0.1501436,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9948992,"about_ca_topic_score_gemma":0.9925451,"domain_scores_codex":[0.9953804,0.0002705997,0.0005011154,0.0005456525,0.002268174,0.001034142],"domain_scores_gemma":[0.9635034,0.001231769,0.001091319,0.0009643945,0.03161914,0.001590069],"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":[0.00002151885,0.000006096734,0.0009001208,0.0002586426,0.00001859863,0.000007016301,0.00002026255,0.0001052811,0.000009354136,0.0003767922,0.9967465,0.001529835],"study_design_scores_gemma":[0.0001349545,0.00001147637,0.02130792,0.0008224397,0.00006556245,0.0000263148,0.0004313383,0.0003572762,0.0001596588,0.000614998,0.9759913,0.00007679704],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00004494324,0.00005823342,0.00002505701,0.0001339761,0.00003281833,0.00001510488,0.9986637,0.00005312392,0.0009731047],"genre_scores_gemma":[0.0009801851,0.0004294898,0.0004473808,0.000197902,0.00002559422,0.0001337682,0.9921565,0.0001264754,0.005502644],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.09494682,"threshold_uncertainty_score":0.4166763,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01399569118133444,"score_gpt":0.2157177146667508,"score_spread":0.2017220234854164,"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."}}