{"id":"W4399574028","doi":"10.32614/cran.package.jointcalib","title":"jointCalib: A Joint Calibration of Totals and Quantiles","year":2024,"lang":"en","type":"dataset","venue":"","topic":"Statistical and numerical algorithms","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Quantile; Calibration; Joint (building); Statistics; Mathematics; Econometrics; Engineering","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"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":[0.01073933,0.001846575,0.001923146,0.004926394,0.001285239,0.004369927,0.003420343,0.001669899,0.1801778],"category_scores_gemma":[0.05159885,0.002557617,0.003205064,0.008267594,0.0009377644,0.00503117,0.005787891,0.004211179,0.07446191],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00256391,"about_ca_system_score_gemma":0.004578589,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02708472,"about_ca_topic_score_gemma":0.02131339,"domain_scores_codex":[0.9934927,0.002649158,0.0003845083,0.001105935,0.001876404,0.0004912362],"domain_scores_gemma":[0.9867141,0.004972921,0.0009149423,0.004004724,0.003059472,0.0003339621],"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":[0.0003358539,0.0001481917,0.02003721,0.0006124021,0.0004583068,0.0001304911,0.0009330833,0.04106884,0.001471932,0.06892096,0.5751387,0.2907439],"study_design_scores_gemma":[0.0002714822,0.00008899315,0.02379869,0.0003167498,0.0001675474,0.0003489905,0.0003974408,0.1362983,0.005971143,0.1355706,0.6964169,0.000353214],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"dataset","genre_scores_codex":[0.004289824,0.0004589081,0.8056767,0.0006077678,0.0003604365,0.0003383949,0.04975338,0.1157454,0.02276921],"genre_scores_gemma":[0.05985789,0.0007260692,0.713572,0.0009097573,0.0003133982,0.002926458,0.07749369,0.1091292,0.03507151],"genre_candidate":"dataset","genre_consensus":null,"teacher_disagreement_score":0.1801778,"threshold_uncertainty_score":0.6027548,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06756158926776032,"score_gpt":0.3334013959208788,"score_spread":0.2658398066531185,"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."}}