{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006814075,0.0006724354,0.0002218155,0.0009860289,0.0002417347,0.0003621961,0.0004028518,0.0007168479,0.0008840291],"category_scores_gemma":[0.0007669225,0.0001534655,0.0002842465,0.0002282397,0.0003786698,0.0002782102,0.0003046615,0.0005170518,0.0004576077],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001422187,"about_ca_system_score_gemma":0.0001373419,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000243682,"about_ca_topic_score_gemma":0.0002593602,"domain_scores_codex":[0.9995481,0.0001171759,0.00002859643,0.00007714221,0.0001564504,0.00007246804],"domain_scores_gemma":[0.9995091,0.0001468943,0.000110351,0.00003221287,0.0001251699,0.00007623613],"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":[0.00006892782,0.00001933607,0.0003372343,0.00003060859,0.00000531078,0.0000424034,0.00001998719,0.00009502664,0.9950373,0.0001656733,0.00003828951,0.004140004],"study_design_scores_gemma":[0.000007959582,0.0003458603,0.003409228,0.00001187214,0.00001501868,0.0002546613,0.00004051982,0.001907544,0.9918118,0.0001357865,0.00204979,0.00001006882],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9480888,0.003779039,0.03856631,0.0002162591,0.000114629,0.0001425422,0.0001880439,0.000288237,0.008616148],"genre_scores_gemma":[0.9369239,0.002123457,0.05317584,0.0002010066,0.00005556195,0.000126664,0.0005589755,0.00005096046,0.006783585],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.0009860289,"threshold_uncertainty_score":0.003603637,"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."}}