{"id":"W6957630354","doi":"10.6068/dp14ba82c128c31","title":"Trend 1980 - 2009. Statistics Canada. CANSIM: Agriculture - Food and Nutrition | Country: Canada | Table: Nutrients in the food supply, by source of nutritional equivalent and commodity | Variable: Manioc, Calcium, Nutrients available adjusted for losses | Units: Milligrams, 1980-2009. Data-Planet™ Statistical Ready Reference by Conquest Systems, Inc. Dataset-ID: 075-001-005.","year":2015,"lang":"en","type":"other","venue":"Data Planet","topic":"Legal case studies and regulations","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Agriculture; Economic statistics; Census; Official statistics; Commodity; Food security; Summary statistics; Food supply","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":[0.0005878534,0.0004827341,0.0007439774,0.00006905128,0.0005944668,0.0002247723,0.001040074,0.0004141419,0.0002924938],"category_scores_gemma":[0.0001145901,0.0003842416,4.486577e-7,0.0004565775,0.0005216089,0.0002226919,0.0003319783,0.0004512473,0.000001488209],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002337172,"about_ca_system_score_gemma":0.003817123,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9991122,"about_ca_topic_score_gemma":0.9992762,"domain_scores_codex":[0.9961005,0.0004536411,0.0006780229,0.0007892977,0.001269549,0.0007089495],"domain_scores_gemma":[0.9971881,0.0009405818,0.0005145261,0.0008440595,0.0001604066,0.0003523625],"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":[0.00009548162,0.0003340859,0.00003780814,0.0008513678,0.0001997982,0.00002437829,0.00002230424,0.000002204697,7.365375e-7,0.004104472,0.9943058,0.00002151511],"study_design_scores_gemma":[0.001846692,0.0002292074,0.000006104136,0.0001191196,0.0003339291,0.0000306374,0.002061571,0.0001562338,6.038174e-8,0.000003345396,0.9947316,0.0004814703],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.000002604656,0.01293537,0.00001336767,0.00003508497,0.0003317519,0.001620201,0.9838924,0.00002276976,0.001146417],"genre_scores_gemma":[0.0001407008,0.003804441,0.00009185685,0.0001740888,0.0002900615,0.00007846168,0.9937615,0.00005982711,0.001599095],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.009869035,"threshold_uncertainty_score":0.9998609,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03516964087981854,"score_gpt":0.257975043657574,"score_spread":0.2228054027777555,"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."}}