{"id":"W2474897245","doi":"10.1145/2911451.2914685","title":"Sampling Strategies and Active Learning for Volume Estimation","year":2016,"lang":"en","type":"article","venue":"","topic":"Machine Learning and Algorithms","field":"Computer Science","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada; University of Waterloo; Google; National Science Foundation","keywords":"Computer science; Leverage (statistics); Popularity; Sampling (signal processing); Switchover; Volume (thermodynamics); Social media; Recall; Active learning (machine learning); Data science; Machine learning; Information retrieval; Precision and recall; Point (geometry); Data mining; Artificial intelligence; World Wide Web","routes":{"ca_aff":true,"ca_fund":true,"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":[0.008499995,0.002215531,0.002401928,0.003777265,0.001014424,0.002964556,0.004154347,0.002669049,0.00156747],"category_scores_gemma":[0.05204199,0.001218868,0.001240125,0.002573739,0.002297289,0.006249267,0.002265879,0.003051934,0.0008548073],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001527266,"about_ca_system_score_gemma":0.00106583,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003400332,"about_ca_topic_score_gemma":0.002900978,"domain_scores_codex":[0.9954489,0.002374916,0.0002828256,0.0008285981,0.0008711342,0.0001937122],"domain_scores_gemma":[0.9650976,0.02843296,0.001464061,0.002602543,0.001971667,0.0004310804],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003913287,0.0003489652,0.007239234,0.0003680021,0.0003032683,0.0002077149,0.0005554201,0.5769531,0.005747442,0.1074192,0.004476989,0.2959893],"study_design_scores_gemma":[0.0000271251,0.00004339745,0.0002717747,0.00002351129,0.00001665192,0.00005061613,0.00002152279,0.9490084,0.001507742,0.04794266,0.001068191,0.00001849734],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005191592,0.0005265993,0.9930466,0.0002267005,0.00003857731,0.00007231956,0.00007686749,0.00034965,0.0004711435],"genre_scores_gemma":[0.3795816,0.001446198,0.6109102,0.0005635581,0.0009202724,0.0008177505,0.001473291,0.0003854779,0.003901507],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008499995,"threshold_uncertainty_score":0.04495281,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01849890833439669,"score_gpt":0.2883151042586092,"score_spread":0.2698161959242125,"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."}}