{"id":"W7000844221","doi":"","title":"Heterogeneity in Prediction Research: methods and applications","year":2017,"lang":"en","type":"dissertation","venue":"Data Archiving and Networked Services (DANS)","topic":"Statistical Methods in Epidemiology","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Cancer Institute; Economic and Social Research Council; Canadian Institutes of Health Research; Erasmus Universitair Medisch Centrum Rotterdam; Genentech; Heart and Stroke Foundation of Canada; Department for International Development; Boston Scientific Corporation; Ontario Ministry of Health and Long-Term Care; Institute for Clinical Evaluative Sciences; National Institutes of Health; Nederlandse Organisatie voor Wetenschappelijk Onderzoek; Wellcome Trust","keywords":"Nucleofection; Gestational period; TSG101; Diafiltration; Dysgeusia; Hyporeflexia; Proteogenomics; Liquation","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.1511615,0.002136715,0.003764039,0.009064665,0.002102131,0.006551395,0.004211464,0.004241972,0.005193831],"category_scores_gemma":[0.2905095,0.001942627,0.003239975,0.01370497,0.007274053,0.006597549,0.006863144,0.008510063,0.001711705],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00391282,"about_ca_system_score_gemma":0.005952556,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005978944,"about_ca_topic_score_gemma":0.003212344,"domain_scores_codex":[0.872845,0.1079262,0.003592343,0.007423729,0.007340035,0.0008725531],"domain_scores_gemma":[0.5051368,0.4628949,0.007870814,0.0153558,0.007693452,0.001048109],"domain_codex":null,"domain_gemma":"methods","domain_candidate":"methods","domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000131283,0.0002171852,0.01646656,0.001612264,0.001176602,0.0002991688,0.001360528,0.02103067,0.0001351227,0.666541,0.02489298,0.2661367],"study_design_scores_gemma":[0.00006887325,0.00005880236,0.001869945,0.0007553881,0.0001021047,0.000113748,0.0002376729,0.0437872,0.0001202699,0.9368263,0.01599762,0.00006201102],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003515637,0.027411,0.9448003,0.01628834,0.0008360943,0.0006513012,0.0005283399,0.0003395779,0.005629374],"genre_scores_gemma":[0.1582472,0.03604168,0.7834724,0.005357108,0.004688792,0.006351287,0.001076777,0.0004121516,0.004352555],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.8488384,"threshold_uncertainty_score":0.7994281,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3595701847584492,"score_gpt":0.581282411860043,"score_spread":0.2217122271015939,"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."}}