{"id":"W2296282946","doi":"10.1002/prca.201500071","title":"Proteomic profiling of eccrine sweat reveals its potential as a diagnostic biofluid for active tuberculosis","year":2016,"lang":"en","type":"article","venue":"PROTEOMICS - CLINICAL APPLICATIONS","topic":"Tuberculosis Research and Epidemiology","field":"Medicine","cited_by":57,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Grand Challenges Canada","keywords":"Eccrine sweat; Tuberculosis; SWEAT; Profiling (computer programming); Medicine; Computational biology; Biology; Pathology; Computer science; Internal medicine","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.001489209,0.000254403,0.0008814334,0.0001578955,0.0001450841,0.00001068744,0.0003235515,0.0003550345,0.00009649735],"category_scores_gemma":[0.01644513,0.0001738728,0.0004970572,0.0002823619,0.0003811452,0.000111268,0.0001740782,0.0003654999,0.0002986151],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001284391,"about_ca_system_score_gemma":0.0003622514,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003148944,"about_ca_topic_score_gemma":0.000002403649,"domain_scores_codex":[0.9966871,0.0002267472,0.001428936,0.0007871649,0.0002612774,0.0006087769],"domain_scores_gemma":[0.994257,0.003408285,0.0003945182,0.0007423555,0.0007103952,0.0004873847],"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":[0.002219475,0.001513691,0.03387563,0.0005179468,0.0006338421,0.000003495774,0.00002031338,0.000004480349,0.8994084,0.01199483,0.00114451,0.04866341],"study_design_scores_gemma":[0.009776743,0.003574254,0.06981213,0.0006204874,0.0009870451,0.00008538435,0.00003791256,0.001070448,0.8551584,0.04837572,0.009767431,0.0007339949],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8429314,0.0002571674,0.1032213,0.0349993,0.00008708553,0.01739344,0.0005840117,0.0001174288,0.0004087627],"genre_scores_gemma":[0.9118773,0.002746117,0.06126926,0.0009710808,0.0008816979,0.02157724,0.0001404072,0.00007609693,0.0004607858],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06894584,"threshold_uncertainty_score":0.9918398,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05599164375948541,"score_gpt":0.4150013432713238,"score_spread":0.3590096995118384,"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."}}