{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01626273,0.002024083,0.001803193,0.002869062,0.001394327,0.00586376,0.003233937,0.006935321,0.01459513],"category_scores_gemma":[0.03090448,0.0009652522,0.001294991,0.001540109,0.00212269,0.008644121,0.005105524,0.009395381,0.02154974],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003911647,"about_ca_system_score_gemma":0.004178796,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001344838,"about_ca_topic_score_gemma":0.001436505,"domain_scores_codex":[0.9924446,0.002682608,0.0005083447,0.001269929,0.002577314,0.0005171816],"domain_scores_gemma":[0.9823631,0.007459496,0.000643217,0.002264803,0.00586993,0.00139931],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0005772819,0.00009069192,0.001960454,0.0006627393,0.0001341322,0.00122311,0.0002069477,0.001312877,0.01424584,0.0753395,0.3842837,0.5199627],"study_design_scores_gemma":[0.0001613936,0.0002131477,0.0007477121,0.0003848998,0.0000923195,0.002795948,0.00009483247,0.01025847,0.00722102,0.1001438,0.8777835,0.0001029746],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"commentary","genre_scores_codex":[0.006423042,0.08299521,0.3089556,0.5373967,0.02309499,0.0005360316,0.00252216,0.009024702,0.02905155],"genre_scores_gemma":[0.108446,0.06508192,0.4700009,0.2795597,0.04143483,0.001286427,0.005906299,0.001709189,0.02657464],"genre_candidate":"commentary","genre_consensus":"commentary","teacher_disagreement_score":0.01626273,"threshold_uncertainty_score":0.08600652,"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."}}