{"id":"W2952658960","doi":"10.1101/354811","title":"Reproducible Data Analysis Pipelines for Precision Medicine","year":2018,"lang":"en","type":"preprint","venue":"bioRxiv (Cold Spring Harbor Laboratory)","topic":"Scientific Computing and Data Management","field":"Decision Sciences","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Canadian Cancer Society Research Institute","keywords":"Computer science; Pipeline (software); Pipeline transport; Precision medicine; Data science; Data mining; Process (computing); Software; Field (mathematics); Engineering; Medicine","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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","metaepi_narrow","scholarly_communication","open_science"],"consensus_categories":["metaresearch","open_science"],"category_scores_codex":[0.03615303,0.0006395329,0.001384993,0.002245889,0.000514379,0.001426111,0.009051282,0.0003612736,0.0004524768],"category_scores_gemma":[0.03674754,0.0004969752,0.0003373023,0.005604223,0.0004113691,0.0004923249,0.009318329,0.0004068529,0.0003689709],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001229662,"about_ca_system_score_gemma":0.0004131007,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001488097,"about_ca_topic_score_gemma":0.00002533596,"domain_scores_codex":[0.9869324,0.0003949032,0.002138846,0.006962382,0.002831439,0.0007400297],"domain_scores_gemma":[0.9681983,0.001608319,0.001583954,0.02509751,0.003124244,0.0003876813],"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":[0.0001350045,0.0003006909,0.0112269,0.0001942502,0.001322147,0.00002016605,0.00003950178,0.001532373,0.01061428,0.0005477344,0.9737529,0.0003140429],"study_design_scores_gemma":[0.001060187,0.0001736468,0.06999352,0.0005215866,0.00290482,1.211501e-8,0.00002950059,0.2096458,0.007776515,0.0004426597,0.7058203,0.001631398],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1406069,0.002239847,0.8257359,0.004838185,0.0186163,0.002715227,0.004232025,0.0008992249,0.0001164142],"genre_scores_gemma":[0.9016439,0.0001451089,0.09165434,0.0004801822,0.00528214,0.0001481706,0.00003165254,0.0001225344,0.0004920166],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.761037,"threshold_uncertainty_score":0.9997482,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1863166794700412,"score_gpt":0.3838333819701594,"score_spread":0.1975167025001182,"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."}}