{"id":"W6976539807","doi":"10.6068/dp14ba85179dd86","title":"Trend 1976 - 2009. Statistics Canada. CANSIM: Agriculture - Livestock and Aquaculture | Country: Canada | Table: Nutrients in the food supply, by source of nutritional equivalent and commodity | Variable: Wheat flour, Energy, Nutrients available adjusted for losses | Units: Kilocalories, 1976-2009. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. Dataset-ID: 075-001-007.","year":2015,"lang":"en","type":"other","venue":"Data Planet","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Livestock; Agriculture; Economic statistics; Census; Commodity; Official statistics; Statistical analysis; Socioeconomic status","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":[0.001836212,0.002311064,0.002548418,0.007851791,0.002977049,0.004291308,0.004756913,0.001417038,0.09702512],"category_scores_gemma":[0.0144648,0.001643391,0.002149969,0.03924418,0.0006374849,0.00237983,0.002111106,0.002871752,0.049966],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.05719642,"about_ca_system_score_gemma":0.1436079,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9954448,"about_ca_topic_score_gemma":0.9935613,"domain_scores_codex":[0.9962665,0.0002243687,0.0003975951,0.0004653753,0.00180673,0.0008393393],"domain_scores_gemma":[0.9716548,0.0009474691,0.0008905985,0.0007170886,0.0244674,0.001322622],"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":[0.00002634422,0.000006733338,0.001194327,0.0003112329,0.00002451197,0.000007134865,0.00002078374,0.0001229321,0.00001228539,0.0004402659,0.9960161,0.001817293],"study_design_scores_gemma":[0.0001459595,0.00001264966,0.02650569,0.0009084754,0.00007408635,0.0000274875,0.0004367495,0.0004173898,0.0001845905,0.0006320121,0.9705731,0.00008187265],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00005319613,0.00006005424,0.00002297619,0.0001184364,0.00002736211,0.00001451248,0.9986631,0.0000447336,0.000995702],"genre_scores_gemma":[0.001153,0.0004820771,0.0004904314,0.0002004199,0.00002036367,0.0001449003,0.9914101,0.0001182501,0.005980461],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.09702512,"threshold_uncertainty_score":0.414991,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02706925001082077,"score_gpt":0.2307659955428958,"score_spread":0.203696745532075,"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."}}