{"id":"W4399203487","doi":"10.1080/10618600.2024.2362219","title":"Iterated Data Sharpening","year":2024,"lang":"en","type":"article","venue":"Journal of Computational and Graphical Statistics","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia, Okanagan Campus; University of British Columbia","funders":"Natural Sciences and Engineering Research Council of Canada; Canada Foundation for Innovation","keywords":"Sharpening; Computer science; Artificial intelligence; Mathematics; Algorithm","routes":{"ca_aff":true,"ca_fund":true,"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":[0.008750247,0.001419534,0.0020667,0.002216696,0.000845318,0.001904795,0.003076048,0.002069045,0.00423743],"category_scores_gemma":[0.0442387,0.0009518412,0.002299585,0.001669801,0.001828386,0.002554261,0.004031587,0.00340561,0.001936087],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008557066,"about_ca_system_score_gemma":0.001675743,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001826965,"about_ca_topic_score_gemma":0.001847806,"domain_scores_codex":[0.9947865,0.002214068,0.0004716118,0.001074721,0.001177432,0.0002755756],"domain_scores_gemma":[0.9765251,0.01282312,0.001540274,0.005384943,0.003423371,0.0003032089],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0008226645,0.0003190944,0.006463809,0.001028637,0.000493856,0.0004091759,0.001176686,0.2413843,0.04710883,0.05889156,0.00418349,0.637718],"study_design_scores_gemma":[0.0000953326,0.0003071186,0.002121704,0.0001319534,0.0001371931,0.0003604732,0.000143762,0.9120208,0.04549563,0.03082477,0.008237676,0.0001235865],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01096639,0.0001750685,0.9872185,0.00006487834,0.00003262907,0.00007270432,0.00006765984,0.0009037953,0.0004984175],"genre_scores_gemma":[0.195042,0.000227902,0.8015143,0.0002161312,0.00004743781,0.0003354955,0.0005289713,0.000529018,0.001558843],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008750247,"threshold_uncertainty_score":0.04627627,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1454890845383303,"score_gpt":0.4105353640326388,"score_spread":0.2650462794943085,"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."}}