{"id":"W2787391845","doi":"10.1007/s00216-018-0934-9","title":"Analytical challenges in sports drug testing","year":2018,"lang":"en","type":"article","venue":"Analytical and Bioanalytical Chemistry","topic":"Hormonal and reproductive studies","field":"Medicine","cited_by":17,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"World Anti-Doping Agency","keywords":"Variety (cybernetics); Athletes; Sample (material); Consistency (knowledge bases); Drug; Drug detection; Risk analysis (engineering); Control (management); Drug control; Psychology; Biochemical engineering; Computer science; Data science; Medicine; Management science; Pharmacology; Engineering; Chemistry; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004111927,0.0002817043,0.0006652125,0.00008439343,0.00009168706,0.0000204501,0.00008934902,0.0001542923,0.0002445109],"category_scores_gemma":[0.0009930518,0.0001965678,0.000133474,0.0005569452,0.001266598,0.00005274534,0.0001655757,0.0004101417,0.00003590643],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005290717,"about_ca_system_score_gemma":0.00007355279,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002334088,"about_ca_topic_score_gemma":0.000009497804,"domain_scores_codex":[0.9977166,0.00001808837,0.0004611395,0.0008133258,0.000457523,0.0005333439],"domain_scores_gemma":[0.9987447,0.0001842218,0.00004439806,0.0003216355,0.0002598435,0.0004451906],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0009085343,0.001932905,0.8888165,0.001166171,0.0006624241,0.001870677,0.0004187746,8.457832e-7,0.00361942,0.006727377,0.001150184,0.09272613],"study_design_scores_gemma":[0.002519855,0.0005884123,0.9368395,0.0006757684,0.001193308,0.0007891477,0.00245594,0.02795764,0.008562495,0.005749624,0.01146569,0.001202572],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8441448,0.00161148,0.000004729433,0.01106757,0.00002734764,0.0001031999,0.000003054825,0.00006241422,0.1429754],"genre_scores_gemma":[0.9954997,0.0004335971,0.000241376,0.0001852745,0.0008828228,0.000004142572,0.000004002734,0.00001672119,0.002732316],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.151355,"threshold_uncertainty_score":0.8015804,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05879872921311066,"score_gpt":0.2985355461371204,"score_spread":0.2397368169240097,"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."}}