{"id":"W2296163019","doi":"10.1186/s13104-016-1965-y","title":"Lessons learned from respondent-driven sampling recruitment in Nairobi: experiences from the field","year":2016,"lang":"en","type":"article","venue":"BMC Research Notes","topic":"HIV, Drug Use, Sexual Risk","field":"Medicine","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Centers for Disease Control and Prevention; U.S. President’s Emergency Plan for AIDS Relief; University of Manitoba; University of Washington","keywords":"Respondent; Sampling (signal processing); Field (mathematics); Data science; Medicine; Computer science; Political science; Telecommunications; Mathematics","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","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00157493,0.0001793963,0.0003198364,0.0002096321,0.000237083,0.00008743345,0.0006098199,0.0001654235,0.001303495],"category_scores_gemma":[0.0165554,0.00009703684,0.0000837114,0.0004558527,0.0003113451,0.0001500748,0.0003806799,0.0005977377,0.000507641],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002073019,"about_ca_system_score_gemma":0.000387294,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00646661,"about_ca_topic_score_gemma":0.005749234,"domain_scores_codex":[0.9962339,0.0009039847,0.0003690335,0.0006558638,0.001135337,0.0007018212],"domain_scores_gemma":[0.9744191,0.0242058,0.0000601079,0.0009494573,0.0001467828,0.0002187933],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00315461,0.0004396146,0.278183,0.00003063562,0.0001082181,0.0001437342,0.1080836,0.00001850412,0.2962376,0.0004905196,0.008762524,0.3043474],"study_design_scores_gemma":[0.007660808,0.001700373,0.3963516,0.00332212,0.0000564972,0.000006049547,0.2945909,0.0005249773,0.1945875,0.01489675,0.08551749,0.0007848947],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9567758,0.0006504719,0.0007897937,0.03989766,0.000156341,0.000915826,0.00005165897,0.00005785324,0.0007045298],"genre_scores_gemma":[0.993809,0.0005540496,0.002602906,0.0003644876,0.0005193459,0.0005262445,0.00002202438,0.00003345834,0.001568469],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3035625,"threshold_uncertainty_score":0.9996095,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.6618118887488177,"score_gpt":0.55474931033975,"score_spread":0.1070625784090677,"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."}}