{"id":"W4206410105","doi":"10.1093/jssam/smab057","title":"Neighborhood Bootstrap for Respondent-Driven Sampling","year":2021,"lang":"en","type":"article","venue":"Journal of Survey Statistics and Methodology","topic":"HIV, Drug Use, Sexual Risk","field":"Medicine","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal","funders":"","keywords":"Resampling; Estimator; Leverage (statistics); Sampling (signal processing); Statistics; Consistency (knowledge bases); Computer science; Variance (accounting); Econometrics; Respondent; Tree (set theory); Bootstrap aggregating; Sample (material); Mathematics; Artificial intelligence","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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.005017424,0.0001297852,0.0006921137,0.0001531049,0.00006407671,0.00002264811,0.00007171977,0.0001260689,0.0000566132],"category_scores_gemma":[0.01810412,0.0001114221,0.00007244357,0.0001336681,0.0000811062,0.00004287639,0.00003861075,0.0003265195,0.00000131686],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003271729,"about_ca_system_score_gemma":0.0003342241,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002331309,"about_ca_topic_score_gemma":0.00011984,"domain_scores_codex":[0.9973767,0.00134092,0.0006430505,0.0001822443,0.0002027739,0.0002542748],"domain_scores_gemma":[0.9867529,0.0113817,0.0003791846,0.0001508328,0.001095076,0.0002402629],"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":[0.01168984,0.0008810004,0.3317231,0.0009446365,0.00232976,0.002580905,0.007077232,0.0002048132,0.169485,0.01981834,0.0505839,0.4026815],"study_design_scores_gemma":[0.009084417,0.004119762,0.9004757,0.0001809846,0.00102333,0.005930626,0.005365436,0.0008977995,0.00419963,0.03412668,0.03414278,0.0004528762],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.07749325,0.0008948554,0.9193943,0.0007871751,0.0006554489,0.0001163933,0.000568011,0.000005144572,0.00008537882],"genre_scores_gemma":[0.06700159,0.0008803217,0.9303576,0.0007662867,0.0003341219,0.000002192643,0.00008389315,0.00003264863,0.0005413912],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.5687526,"threshold_uncertainty_score":0.9901668,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5731953672614928,"score_gpt":0.5175135239374811,"score_spread":0.05568184332401171,"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."}}