{"id":"W2958839295","doi":"10.1126/science.aaw2825","title":"Certify reproducibility with confidential data","year":2019,"lang":"en","type":"article","venue":"Science","topic":"Scientific Computing and Data Management","field":"Decision Sciences","cited_by":40,"is_retracted":false,"has_abstract":true,"ca_institutions":"Cascades (Canada)","funders":"Agence Nationale de la Recherche","keywords":"Confidentiality; Microdata (statistics); Certification; Computer science; Accreditation; Internet privacy; Process (computing); Government (linguistics); Data science; Computer security; Medicine; Political science; Environmental health; Medical education","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","scholarly_communication","open_science","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.03969988,0.00009256385,0.000153787,0.0002619406,0.0003204297,0.001509395,0.007617437,0.00001475732,0.0009674854],"category_scores_gemma":[0.007300179,0.00005660635,0.00002064461,0.003108815,0.0008508521,0.001618248,0.003663042,0.00009140907,0.003435979],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002886906,"about_ca_system_score_gemma":0.0003010376,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007979754,"about_ca_topic_score_gemma":0.00005279187,"domain_scores_codex":[0.9915195,0.0000790766,0.0003872,0.004202243,0.003433621,0.0003783439],"domain_scores_gemma":[0.9817241,0.0003110469,0.0001708148,0.01726848,0.0003806897,0.0001448868],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001597504,0.0003780839,0.4788538,0.00002133366,0.00002160742,0.00003223176,0.001309094,0.002066998,0.01804036,0.01473683,0.1021488,0.3822311],"study_design_scores_gemma":[0.0005885414,0.0001688174,0.4227581,0.00003137014,0.00001356781,0.00003479558,0.001636861,0.06118215,0.002690801,0.01045107,0.4999788,0.000465103],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9580217,0.00002844063,0.005505147,0.001703775,0.002358008,0.00029316,0.00002892763,0.00007744871,0.03198339],"genre_scores_gemma":[0.9897619,4.337948e-7,0.002309775,0.0002209473,0.00004567682,9.419334e-7,0.000007414297,0.000003033613,0.007649871],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.39783,"threshold_uncertainty_score":0.9999458,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3086534531217177,"score_gpt":0.4538310386138396,"score_spread":0.145177585492122,"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."}}