{"id":"W2341719468","doi":"10.1039/9781849734363-00334","title":"Discovery and Validation Case Studies, Recommendations: Bottlenecks in Biomarker Discovery and Validation by Using Proteomic Technologies","year":2013,"lang":"en","type":"book-chapter","venue":"","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University Health Network; University of Toronto; Mount Sinai Hospital","funders":"","keywords":"Biomarker discovery; Biomarker; Risk analysis (engineering); Computer science; Government (linguistics); Data science; Plan (archaeology); Bench to bedside; Engineering; Medicine; Proteomics; Biology; Medical physics","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":[0.05286789,0.001764881,0.001184403,0.00235406,0.002003219,0.009226053,0.004868573,0.003212869,0.007490008],"category_scores_gemma":[0.05947118,0.0009861257,0.001049044,0.003275138,0.002503586,0.01079048,0.00344119,0.004688399,0.006515519],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004188403,"about_ca_system_score_gemma":0.006674177,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003974268,"about_ca_topic_score_gemma":0.008398808,"domain_scores_codex":[0.9802554,0.009082243,0.001259475,0.001409989,0.007446771,0.0005461963],"domain_scores_gemma":[0.940935,0.04000829,0.00215172,0.003984352,0.01168229,0.001238313],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001432951,0.0003572592,0.003445937,0.002253125,0.00006809099,0.002375334,0.00264564,0.003127871,0.003206798,0.03748032,0.3330353,0.6118611],"study_design_scores_gemma":[0.0000623704,0.0003678031,0.002913175,0.004079469,0.0001406193,0.004698878,0.003698839,0.008552499,0.00944244,0.06473722,0.9011506,0.0001561159],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02873276,0.150095,0.4272079,0.1727992,0.01087258,0.003777985,0.002168968,0.007078246,0.1972673],"genre_scores_gemma":[0.07784911,0.0782566,0.644028,0.0276531,0.002368663,0.001983429,0.003421369,0.002367175,0.1620725],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.05286789,"threshold_uncertainty_score":0.2795955,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04192069942056894,"score_gpt":0.3133877067613484,"score_spread":0.2714670073407795,"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."}}