{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001238971,0.00005186431,0.00005499939,0.00003183594,0.0001237457,0.0001843766,0.0001003274,0.00001952625,0.00001136182],"category_scores_gemma":[0.0001151565,0.00003267697,0.00001472507,0.00004341347,0.00001521803,0.0006697124,0.00004628372,0.00004332816,0.00001059907],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000008013587,"about_ca_system_score_gemma":0.00002070771,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001989216,"about_ca_topic_score_gemma":0.00000141071,"domain_scores_codex":[0.9995904,0.00001841192,0.0000601002,0.0001637377,0.00005614026,0.0001112082],"domain_scores_gemma":[0.9996593,0.0001615443,0.00003338884,0.00007491001,0.00004201638,0.00002886358],"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":[0.000002050626,0.000003196689,0.0001807509,0.000005008728,0.000003651515,1.482939e-7,0.000242633,0.001575573,0.0005428539,0.04336113,0.0000381732,0.9540448],"study_design_scores_gemma":[0.0002527841,0.00009814958,0.004207589,0.00001699393,0.000001787057,0.000004075831,0.0001310201,0.9773974,0.0003883898,0.01378749,0.003619039,0.00009525779],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01226549,0.00001020434,0.9849398,0.001540698,0.00005791177,0.00005353744,2.839149e-7,0.0001629852,0.0009691424],"genre_scores_gemma":[0.7023575,0.000002087762,0.2955994,0.00003122953,0.00003429757,0.000007267849,7.966858e-7,0.000003270522,0.001964172],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9758219,"threshold_uncertainty_score":0.1777948,"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."}}