{"id":"W2621286621","doi":"10.1159/000460724","title":"How to Develop Intelligence Gathering in Efficient and Practical Anti-Doping Activities","year":2017,"lang":"en","type":"article","venue":"Medicine and sport science/Medicine and sport","topic":"Doping in Sports","field":"Social Sciences","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"World Anti-Doping Agency","funders":"","keywords":"Sanctions; Agency (philosophy); Law enforcement; Code (set theory); Political science; Public relations; Law; Enforcement; Legislation; Law and economics; Computer science; Sociology; Set (abstract data type)","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":[0.02837095,0.002992702,0.001407696,0.008436887,0.008318225,0.021764,0.005198933,0.00675427,0.02048546],"category_scores_gemma":[0.06167669,0.002066508,0.00167261,0.004303515,0.009025519,0.02918182,0.01481257,0.007679766,0.02307634],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005017363,"about_ca_system_score_gemma":0.02114868,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006632609,"about_ca_topic_score_gemma":0.005629806,"domain_scores_codex":[0.9655451,0.01770852,0.002068616,0.003621049,0.008208016,0.0028487],"domain_scores_gemma":[0.9448679,0.02099287,0.004675905,0.007958944,0.01565159,0.005852813],"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":[0.0001762621,0.0008185248,0.003872187,0.002654569,0.0001327933,0.001054479,0.01574405,0.004166158,0.004523181,0.1593469,0.1463259,0.6611849],"study_design_scores_gemma":[0.0001007482,0.0002594695,0.004310959,0.004391713,0.0001156159,0.0006426153,0.03464698,0.005568937,0.004558174,0.1701624,0.7750568,0.0001855638],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.01768325,0.00752875,0.4615221,0.1210956,0.003563697,0.005377495,0.0005414747,0.0057795,0.3769082],"genre_scores_gemma":[0.1626752,0.008546621,0.7528893,0.01186864,0.001058655,0.003821456,0.001736984,0.00120278,0.05620041],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.02837095,"threshold_uncertainty_score":0.1500417,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07178607093014978,"score_gpt":0.3856131623857915,"score_spread":0.3138270914556417,"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."}}