{"id":"W2953642885","doi":"10.1145/3331184.3331354","title":"Dynamic Sampling Meets Pooling","year":2019,"lang":"en","type":"article","venue":"","topic":"Topic Modeling","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"National Institute of Standards and Technology","keywords":"Pooling; NIST; Computer science; Sampling (signal processing); Statistics; Set (abstract data type); Sample (material); Data mining; Information retrieval; Artificial intelligence; Natural language processing; Mathematics; Computer vision","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02229661,0.002258112,0.002069941,0.001820147,0.001922055,0.004109826,0.002184834,0.001659826,0.007684432],"category_scores_gemma":[0.07552332,0.0008710079,0.001799522,0.00253417,0.001449946,0.006243252,0.005588402,0.002102997,0.002788304],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001688313,"about_ca_system_score_gemma":0.002354144,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006194763,"about_ca_topic_score_gemma":0.006894866,"domain_scores_codex":[0.9784669,0.009210346,0.001439122,0.004871687,0.004851156,0.001160844],"domain_scores_gemma":[0.9651088,0.01692784,0.001355598,0.00983224,0.006107547,0.0006679781],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002640522,0.0007006987,0.02354709,0.001299858,0.0007481939,0.0003702567,0.004909141,0.0568947,0.03972726,0.02813959,0.03157566,0.8094471],"study_design_scores_gemma":[0.00104,0.003310461,0.05754635,0.000392764,0.001320729,0.001518313,0.003797805,0.5317167,0.09018052,0.1727242,0.1358166,0.0006356849],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1204438,0.001270782,0.8476253,0.0008900033,0.0004133776,0.002115661,0.001774736,0.004759388,0.02070687],"genre_scores_gemma":[0.6815774,0.0002752077,0.3035741,0.0006633356,0.0004135886,0.002353425,0.002649572,0.001371281,0.007122075],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02229661,"threshold_uncertainty_score":0.1179171,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02051590183612376,"score_gpt":0.2626628086594748,"score_spread":0.2421469068233511,"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."}}