{"id":"W6930949167","doi":"10.5281/zenodo.15832651","title":"Distributed Biomarker Discovery Pipeline","year":2025,"lang":"en","type":"preprint","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Statistical Methods and Applications","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Princess Margaret Cancer Centre","funders":"","keywords":"Pipeline (software); SPARK (programming language); Multivariable calculus; Pipeline transport; Big data; Key (lock); Predictive modelling","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":["metaepi_narrow","insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.0008969603,0.0002552711,0.0003360263,0.0001972831,0.001239267,0.001022326,0.001276275,0.0001882882,0.00474582],"category_scores_gemma":[0.004744613,0.0002541674,0.000123559,0.0004973384,0.0001869693,0.00009090294,0.004141425,0.0006064316,0.0009775598],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001797356,"about_ca_system_score_gemma":0.00001061381,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001575789,"about_ca_topic_score_gemma":2.284878e-7,"domain_scores_codex":[0.997636,0.0005128901,0.0004850386,0.0006518552,0.0003564217,0.0003577663],"domain_scores_gemma":[0.9976791,0.0003240916,0.0002103173,0.001009948,0.0006186237,0.0001578792],"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":[0.00004286518,0.0002935602,7.433757e-7,0.0004845559,0.00009548882,0.000007112649,0.0001485974,0.00001483345,0.000371785,0.2871909,0.607463,0.1038865],"study_design_scores_gemma":[0.0002834319,0.00002832742,0.0001510432,0.0001698521,0.0000907284,0.00001356515,0.00008131685,0.002108615,0.0001753305,0.1725007,0.8241147,0.0002823839],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0005413748,0.00006038304,0.9037341,0.001410518,0.000171127,0.0007938677,0.01165696,0.00092348,0.08070816],"genre_scores_gemma":[0.3126169,0.001119299,0.4762751,0.001309727,0.002098837,0.000004679217,0.1365483,0.01278453,0.05724258],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.427459,"threshold_uncertainty_score":0.9999911,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1316222760555739,"score_gpt":0.3736416402971227,"score_spread":0.2420193642415488,"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."}}