{"id":"W7104519035","doi":"10.5683/sp3/bseqrw","title":"Replication Data for: Faster, Simpler, and More Precise Calibration Curves: Expanding the Scope of Continuous Calibration","year":2025,"lang":"","type":"dataset","venue":"Borealis","topic":"","field":"","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Scope (computer science); Calibration; Replication (statistics); Key (lock)","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007609506,0.002424056,0.001639576,0.00357419,0.001680647,0.0042353,0.005326814,0.003255244,0.04054172],"category_scores_gemma":[0.03991732,0.001056448,0.002365601,0.005289293,0.001091801,0.003321915,0.004089019,0.002988831,0.06089371],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002729993,"about_ca_system_score_gemma":0.004034119,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06970175,"about_ca_topic_score_gemma":0.149109,"domain_scores_codex":[0.9939271,0.001710054,0.0005450554,0.001446873,0.001866335,0.0005046129],"domain_scores_gemma":[0.9829653,0.003237331,0.00130165,0.00676746,0.004676109,0.001052164],"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":[0.0001137219,0.00002335211,0.001792528,0.000300581,0.00005387738,0.00001415254,0.00003255765,0.0003618051,0.0001184289,0.0009588482,0.991352,0.004878318],"study_design_scores_gemma":[0.0003872627,0.00003000884,0.01033629,0.000323168,0.00006368265,0.00005031106,0.0001368154,0.001109949,0.0005769499,0.003796527,0.9830995,0.0000894924],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"dataset","genre_scores_codex":[0.0009303729,0.0004214725,0.00102377,0.001118268,0.0002935233,0.00004850066,0.99013,0.002215745,0.003818349],"genre_scores_gemma":[0.00239058,0.00008304109,0.002701967,0.0002866034,0.00004921838,0.0001448358,0.9921673,0.0004072531,0.001769105],"genre_candidate":"dataset","genre_consensus":"dataset","teacher_disagreement_score":0.06970175,"threshold_uncertainty_score":0.1385921,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0526137024594004,"score_gpt":0.3497999502718774,"score_spread":0.297186247812477,"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."}}