{"id":"W4383347179","doi":"10.31222/osf.io/7ydep","title":"CRRESS - Session 9 - Reproducibility, Confidentiality, and Open Data Mandates","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Research Data Management Practices","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"","keywords":"Confidentiality; Transparency (behavior); Session (web analytics); Reproducibility; Open data; Computer science; Internet privacy; Computer security; World Wide Web; Mathematics; Statistics","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":["metaresearch","open_science"],"consensus_categories":[],"category_scores_codex":[0.07791518,0.001198001,0.001733346,0.001856172,0.007660189,0.02220487,0.004460058,0.01289848,0.2179768],"category_scores_gemma":[0.09118487,0.001163218,0.001816767,0.002427654,0.00344089,0.01284267,0.01397634,0.01266756,0.1697425],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005983625,"about_ca_system_score_gemma":0.014822,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007838015,"about_ca_topic_score_gemma":0.007132874,"domain_scores_codex":[0.9409187,0.02434579,0.003661745,0.00543206,0.0209222,0.00471955],"domain_scores_gemma":[0.9068454,0.02685965,0.00356276,0.02276645,0.02981348,0.01015224],"domain_codex":null,"domain_gemma":"reproducibility","domain_candidate":"reproducibility","domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0002619796,0.00009532604,0.0006863606,0.0003994159,0.00003337437,0.0001717812,0.0009442554,0.0002535043,0.001789329,0.1104339,0.8496947,0.03523608],"study_design_scores_gemma":[0.0000504542,0.00003757545,0.0005368939,0.0003351334,0.00001117815,0.0001274085,0.0003815442,0.0003453039,0.001517841,0.01933347,0.9772757,0.00004739267],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"other","genre_gemma":"other","genre_scores_codex":[0.006991179,0.006226005,0.09183822,0.1888198,0.04591396,0.003994257,0.01286728,0.01193564,0.6314138],"genre_scores_gemma":[0.1116516,0.006002549,0.07327589,0.05713164,0.02791362,0.004731161,0.0314445,0.01542053,0.6724285],"genre_candidate":"other","genre_consensus":"other","teacher_disagreement_score":0.99554,"threshold_uncertainty_score":0.729205,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4991645346284138,"score_gpt":0.5043080894478092,"score_spread":0.005143554819395491,"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."}}