{"id":"W2599342551","doi":"10.1093/jssam/smw035","title":"Adaptive and Network Sampling for Inference and Interventions in Changing Populations","year":2016,"lang":"en","type":"article","venue":"Journal of Survey Statistics and Methodology","topic":"HIV, Drug Use, Sexual Risk","field":"Medicine","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Sampling (signal processing); Sampling design; Inference; Computer science; Population; Adaptive sampling; Sample (material); Smoothing; Data mining; Simple random sample; Tracing; Machine learning; Statistics; Artificial intelligence; 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":[],"consensus_categories":[],"category_scores_codex":[0.004403905,0.00007231625,0.0003660964,0.0002498884,0.00005474437,0.000008800292,0.00002283816,0.00005424361,0.000005269499],"category_scores_gemma":[0.00737996,0.00005048554,0.00002050001,0.000110188,0.00008478666,0.00005082906,0.0000356877,0.0001135138,9.040902e-8],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001774716,"about_ca_system_score_gemma":0.00003333075,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005616627,"about_ca_topic_score_gemma":0.0007290594,"domain_scores_codex":[0.998763,0.0005146709,0.0003983463,0.0001006893,0.00005453822,0.0001687216],"domain_scores_gemma":[0.9915729,0.007835566,0.0002334245,0.00004607207,0.0002119934,0.0001001135],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0007552806,0.00005698059,0.5920383,0.0001535508,0.0001219722,0.0000214042,0.002152626,0.000049912,0.0005918825,0.02359161,0.0003605855,0.3801059],"study_design_scores_gemma":[0.001501676,0.0008040223,0.9555449,0.0004541148,0.0001015557,0.0001246756,0.0009710615,0.0006581207,0.000008042996,0.03956946,0.0001850051,0.00007730879],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.1670178,0.0006719516,0.8316286,0.0002812118,0.0001382717,0.0001042235,0.0001503853,0.000001717843,0.000005798164],"genre_scores_gemma":[0.4251676,0.0006011048,0.5740307,0.00005048231,0.00007635034,0.000002411296,0.000005903719,0.000007370639,0.0000580737],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3800285,"threshold_uncertainty_score":0.8835034,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.6918727978663437,"score_gpt":0.5395960375672009,"score_spread":0.1522767602991428,"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."}}