{"id":"W2125598411","doi":"10.1373/clinchem.2010.155705","title":"Improving the Biomarker Pipeline","year":2010,"lang":"en","type":"letter","venue":"Clinical Chemistry","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Biomarker; Pipeline (software); Computer science; Computational biology; Chemistry; Biology; Biochemistry","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","research_integrity","insufficient_payload"],"consensus_categories":["research_integrity"],"category_scores_codex":[0.0003853709,0.0004368816,0.000475589,0.000008825748,0.0002004719,0.00008015474,0.001419603,0.005002837,0.002311106],"category_scores_gemma":[0.0006564677,0.0003216564,0.0005248047,0.00008780447,0.0005689829,0.00003211237,0.0004186331,0.0141304,0.00009242113],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000450882,"about_ca_system_score_gemma":0.0001384819,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008677896,"about_ca_topic_score_gemma":5.437896e-7,"domain_scores_codex":[0.9973723,0.00001577222,0.001007628,0.0008664697,0.0002694456,0.0004683262],"domain_scores_gemma":[0.9961194,0.000794681,0.0006778638,0.002171756,0.0001289327,0.0001073511],"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":[0.000007348505,0.00003718267,0.0001309469,0.0002706794,0.00002640203,0.00003023355,0.000001227538,5.443427e-8,0.04023828,0.000003857528,0.9452958,0.01395805],"study_design_scores_gemma":[0.0001621755,0.000001662707,9.609323e-7,0.00004803856,0.00007070192,0.00002716329,0.000003069568,0.0002642923,0.1121783,0.0009156068,0.8859548,0.000373217],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"commentary","genre_scores_codex":[0.005350481,0.0003939177,0.005882468,0.9220626,0.0004244173,0.0004325682,0.000538872,0.001054734,0.06385991],"genre_scores_gemma":[0.004184409,0.0003727018,0.01603653,0.7746657,0.04144842,0.00104253,0.003016206,0.0004432289,0.1587902],"genre_candidate":"commentary","genre_consensus":"commentary","teacher_disagreement_score":0.1473969,"threshold_uncertainty_score":0.9999235,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0382626853548671,"score_gpt":0.3468248961585365,"score_spread":0.3085622108036694,"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."}}