{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.08948983,0.001207841,0.001797483,0.002637914,0.001336659,0.002298268,0.003234647,0.002343399,0.005411882],"category_scores_gemma":[0.2693467,0.001037459,0.002118969,0.00239683,0.007183936,0.004288729,0.003731557,0.004987875,0.0003153515],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002691573,"about_ca_system_score_gemma":0.002511389,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004168263,"about_ca_topic_score_gemma":0.003293781,"domain_scores_codex":[0.8744895,0.115962,0.001538378,0.003681037,0.003895711,0.000433335],"domain_scores_gemma":[0.6631449,0.3087742,0.007659683,0.01519088,0.004365028,0.0008653486],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001757855,0.000114946,0.004069144,0.0004774224,0.0004529489,0.0001036234,0.0006251532,0.09926884,0.0003093452,0.8261264,0.001595694,0.06668071],"study_design_scores_gemma":[0.0001242411,0.0001668126,0.0008906093,0.0001762333,0.00006603564,0.00004492421,0.00009847936,0.2842137,0.0003568563,0.709453,0.004374321,0.00003479674],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002656997,0.0003018832,0.9950383,0.0005754548,0.00008374419,0.0002241664,0.00004847172,0.00005671963,0.001014229],"genre_scores_gemma":[0.1319576,0.0009371111,0.8613431,0.0005748388,0.0003079689,0.003551678,0.000165903,0.00005812535,0.001103515],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.08948983,"threshold_uncertainty_score":0.4732731,"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."}}