{"id":"W1989292033","doi":"10.1002/cmdc.200700085","title":"SERS Classification of Highly Related Performance Enhancers","year":2007,"lang":"en","type":"article","venue":"ChemMedChem","topic":"Advanced Proteomics Techniques and Applications","field":"Chemistry","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta; National Research Council Canada; National Institute for Nanotechnology","funders":"Genomic Health","keywords":"Enhancer; Athletes; Performance enhancement; Nanotechnology; Risk analysis (engineering); Computational biology; Computer science; Drug discovery; Biochemical engineering; Chemistry; Medicine; Biology; Engineering; Materials science; Gene; Biochemistry; Transcription factor; Physical medicine and rehabilitation","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":[],"consensus_categories":[],"category_scores_codex":[0.0001527249,0.0001089333,0.0001253384,0.00003426726,0.00005437063,0.000003953239,0.0001955602,0.0001511392,0.0001470215],"category_scores_gemma":[0.0000207035,0.0001144457,0.00005214474,0.0002019582,0.0001065204,0.00007708582,0.00002928085,0.0001820383,0.00002230938],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000112304,"about_ca_system_score_gemma":0.00002902424,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000404356,"about_ca_topic_score_gemma":8.034289e-7,"domain_scores_codex":[0.999139,0.000001363857,0.0003278418,0.0002087633,0.0001221675,0.0002008501],"domain_scores_gemma":[0.9992403,0.00003670254,0.0002110727,0.0003723064,0.00007619276,0.0000634734],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0000182307,0.00003230353,0.0009220435,0.00007014954,0.000009054651,2.746724e-7,0.00008581935,0.00000421096,0.9849427,0.002550951,0.0001477295,0.0112165],"study_design_scores_gemma":[0.000178456,0.000007771513,0.0006169069,0.00003642474,0.00000892976,0.000002848613,0.0001058348,0.0002563207,0.9934845,0.0008355423,0.004341666,0.0001247758],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9006953,0.00004961757,0.005797108,0.0001191982,0.00001595399,0.0000795931,0.000004559141,0.0001625711,0.09307614],"genre_scores_gemma":[0.9851714,0.0000901073,0.0128996,0.00002059179,0.00003713483,0.00004026827,0.00004349055,0.00002120665,0.001676218],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09139992,"threshold_uncertainty_score":0.4666959,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01466371528327035,"score_gpt":0.270002215844357,"score_spread":0.2553385005610866,"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."}}