{"id":"W6901639691","doi":"10.6068/dp14ba872cf7319","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: Asparagus, canned, Vitamin K, Nutrients available adjusted for losses | Units: Micrograms, 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":[0.001794398,0.002475722,0.002648372,0.007673329,0.003031946,0.004444916,0.00478075,0.001581752,0.107991],"category_scores_gemma":[0.01498776,0.001666104,0.002231539,0.03829484,0.0006549809,0.002410532,0.00214002,0.00292073,0.05843576],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.05147598,"about_ca_system_score_gemma":0.1304552,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9946114,"about_ca_topic_score_gemma":0.9926563,"domain_scores_codex":[0.9965273,0.0002123438,0.0003916943,0.0004794853,0.001617678,0.0007716381],"domain_scores_gemma":[0.9729449,0.0009934509,0.0008928425,0.0007530525,0.02313474,0.001280985],"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":[0.00002626234,0.000006396891,0.00109936,0.0003055509,0.00002431753,0.000006680284,0.00001834115,0.0001173646,0.00001224405,0.0003697406,0.9963573,0.001656476],"study_design_scores_gemma":[0.0001620812,0.00001275893,0.02327749,0.0009209343,0.0000729211,0.0000265858,0.0003863072,0.0004196319,0.0001804863,0.0006368883,0.9738196,0.00008425334],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.00004415878,0.00005355196,0.00002005376,0.0001029435,0.00002516996,0.00001255645,0.9988351,0.00004374203,0.0008627424],"genre_scores_gemma":[0.0009025614,0.0004005321,0.0004143928,0.0001783055,0.00001863476,0.0001287844,0.9931061,0.0001125488,0.004738254],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.107991,"threshold_uncertainty_score":0.3734861,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02739824496408066,"score_gpt":0.2379477930972523,"score_spread":0.2105495481331716,"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."}}