{"id":"W4366393490","doi":"10.1002/lrh2.10365","title":"Toward a common standard for data and specimen provenance in life sciences","year":2023,"lang":"en","type":"article","venue":"Learning Health Systems","topic":"Scientific Computing and Data Management","field":"Decision Sciences","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Standards Association; Queen's University; Ontario Institute for Cancer Research","funders":"National Institute of Biomedical Imaging and Bioengineering; National Cancer Institute; Engineering and Physical Sciences Research Council; Horizon 2020 Framework Programme; National Institutes of Health; Bundesministerium für Bildung, Wissenschaft und Forschung; National Institute of Standards and Technology; European Bioinformatics Institute; U.S. Environmental Protection Agency; King's College London; National Institute of General Medical Sciences; National Institute for Health and Care Research; Medical Research Council; Chan Zuckerberg Initiative; EOSC-Life; Alan Turing Institute; Wellcome Trust; Silicon Valley Community Foundation; National Science Foundation","keywords":"Documentation; Standardization; Traceability; Reuse; Computer science; Data science; Data sharing; Quality (philosophy); Data collection; Knowledge management; Engineering; Software engineering; Medicine","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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.03753064,0.0001049162,0.0003937118,0.0004595341,0.000578696,0.0006869512,0.001321936,0.00003077231,0.000005614856],"category_scores_gemma":[0.00514521,0.00008010736,0.00001967142,0.001845933,0.0001171678,0.0003042007,0.0009121409,0.0001475201,0.00008633635],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005568605,"about_ca_system_score_gemma":0.0002813087,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007640813,"about_ca_topic_score_gemma":0.0001261159,"domain_scores_codex":[0.9958989,0.0004783096,0.0008415196,0.001087668,0.001179357,0.0005142401],"domain_scores_gemma":[0.9967259,0.00175126,0.0004032093,0.0008872135,0.00006837438,0.0001640472],"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":[0.00008380361,0.00003593548,0.1461581,0.0006306713,0.00001234364,0.0000124497,0.005970899,0.02428223,0.000005473063,0.007645125,0.5997581,0.2154049],"study_design_scores_gemma":[0.0003167531,0.0001739058,0.008446545,0.0001756778,0.000001067978,0.000002076067,0.00648374,0.479751,3.009615e-7,0.0006946024,0.5038637,0.00009065352],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7792998,0.01229891,0.09921603,0.07526475,0.01511154,0.008506187,0.0009058533,0.001368308,0.008028635],"genre_scores_gemma":[0.9960173,0.00008074271,0.001003078,0.0002279184,0.0001618818,0.00003310274,0.00005383971,0.00000991843,0.002412228],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4554687,"threshold_uncertainty_score":0.9910647,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4555968385108442,"score_gpt":0.4982650753299125,"score_spread":0.04266823681906828,"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."}}