{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.05385251,0.002725932,0.00190936,0.005016823,0.002314294,0.009082441,0.005819356,0.002036742,0.01018151],"category_scores_gemma":[0.1131746,0.002799666,0.004511696,0.004380446,0.003641076,0.007592719,0.009965239,0.007224865,0.009847721],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002836331,"about_ca_system_score_gemma":0.01145389,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005284136,"about_ca_topic_score_gemma":0.003807287,"domain_scores_codex":[0.9643793,0.01079003,0.00510312,0.008211251,0.01027989,0.001236474],"domain_scores_gemma":[0.8796199,0.03305425,0.007556526,0.0527024,0.02416121,0.00290576],"domain_codex":null,"domain_gemma":"reproducibility","domain_candidate":"reproducibility","domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.002732785,0.0004259038,0.02270403,0.002515083,0.002112835,0.001088215,0.002430823,0.06619545,0.06313978,0.1114884,0.2063447,0.518822],"study_design_scores_gemma":[0.000774148,0.0003692283,0.007265688,0.0007760551,0.000477865,0.0005644298,0.0003725293,0.3970919,0.1308052,0.2059819,0.2548858,0.0006353748],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002471858,0.0003992413,0.8902962,0.001257758,0.0002804503,0.0004350678,0.002997925,0.1002402,0.001621414],"genre_scores_gemma":[0.05073556,0.0004337298,0.9157179,0.0009872733,0.0002479108,0.001005552,0.01143765,0.01809035,0.00134401],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.9461475,"threshold_uncertainty_score":0.2848027,"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."}}