{"id":"W4399572216","doi":"10.32614/cran.package.statcanr","title":"statcanR: Client for Statistics Canada's Open Economic Data","year":2019,"lang":"en","type":"dataset","venue":"","topic":"demographic modeling and climate adaptation","field":"Decision Sciences","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Economic statistics; Statistics; Computer science; Data science; Mathematics","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":["scholarly_communication","open_science","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.003464499,0.0002683098,0.0006211133,0.0001839121,0.0001587373,0.001182117,0.007616473,0.0001476738,0.002422068],"category_scores_gemma":[0.001115614,0.000205715,0.0000412565,0.0001389844,0.00003869045,0.0003182947,0.001730347,0.0001692102,0.0005834394],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001520868,"about_ca_system_score_gemma":0.003997439,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.7489042,"about_ca_topic_score_gemma":0.9796587,"domain_scores_codex":[0.9961047,0.0001023668,0.001162063,0.001268825,0.0009950165,0.0003669897],"domain_scores_gemma":[0.9924898,0.002363909,0.0006996248,0.003995409,0.0002851026,0.0001661788],"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":[0.00003062204,0.00001659274,0.000009137155,0.00001708862,0.00003453042,0.000001813099,0.000002838029,0.001027728,2.943728e-8,0.0003351754,0.9937417,0.004782761],"study_design_scores_gemma":[0.0003445052,0.00005151915,0.00002371042,0.00001101178,0.00005854112,0.000001302277,0.0002137931,0.03826731,1.564639e-7,0.002918225,0.9578251,0.0002848606],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.000007405438,0.0000311046,0.0202281,0.0003364225,0.002642783,0.0008999304,0.9755226,0.000006637019,0.0003250293],"genre_scores_gemma":[0.00001410718,0.0001623887,0.00608138,0.0009758039,0.0001021349,0.00003509401,0.9882308,0.00001760554,0.004380689],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.2307545,"threshold_uncertainty_score":0.9998547,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4295815908096442,"score_gpt":0.4857102101632895,"score_spread":0.05612861935364533,"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."}}