{"id":"W6976699719","doi":"10.6068/dp14ba82b6ae871","title":"Trend 1965 - 2010. Statistics Canada. CANSIM: Manufacturing - Food, Beverage and Tobacco | Country: Canada | Table: Food available by major groups in Canada | Variable: Total creams, Food available adjusted for losses | Units: Litres per person, per year, 1965-2010. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. Dataset-ID: 075-001-151.","year":2015,"lang":"en","type":"other","venue":"Data Planet","topic":"Extraction and Separation Processes","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Economic statistics; Census; Official statistics; Summary statistics; Agriculture; Social statistics; Statistical analysis; Food processing; Food industry","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":[0.002008116,0.002368165,0.002691223,0.008714143,0.003055429,0.00461434,0.005113622,0.00149476,0.1013399],"category_scores_gemma":[0.0168028,0.001768097,0.002370054,0.04115471,0.0006949982,0.002668243,0.002248108,0.003175788,0.06008872],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.05556038,"about_ca_system_score_gemma":0.142084,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9947896,"about_ca_topic_score_gemma":0.9925597,"domain_scores_codex":[0.9958573,0.0002354126,0.0004332825,0.0004847423,0.002059586,0.0009296012],"domain_scores_gemma":[0.967658,0.001002936,0.0009918147,0.0008553231,0.02800148,0.001490423],"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":[0.00002351824,0.000006216043,0.0008733542,0.0002824231,0.00002035301,0.000006470595,0.00001990018,0.00009756104,0.000009755139,0.0003758138,0.9966332,0.001651571],"study_design_scores_gemma":[0.0001384609,0.0000122082,0.02263637,0.0007876375,0.00006930697,0.00002645437,0.0003847985,0.0003368152,0.0001683321,0.0005756565,0.9747853,0.00007872008],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00004791594,0.0000602976,0.00002493004,0.0001189052,0.00003343908,0.00001613024,0.9985933,0.0000576674,0.001047476],"genre_scores_gemma":[0.0009540074,0.0004048384,0.0004426467,0.0001851664,0.00002347349,0.0001362405,0.9920768,0.0001348083,0.005642021],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.1013399,"threshold_uncertainty_score":0.4031206,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02352315643872086,"score_gpt":0.2111553175951517,"score_spread":0.1876321611564308,"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."}}