{"id":"W2999671783","doi":"10.1109/ipccc47392.2019.8958773","title":"Automatic Data Quality Enhancement with Expert Knowledge for Mobile Crowdsensing","year":2019,"lang":"en","type":"article","venue":"","topic":"Mobile Crowdsensing and Crowdsourcing","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University; University of Victoria","funders":"","keywords":"Crowdsensing; Computer science; Crowdsourcing; Voting; Probabilistic logic; Ground truth; Machine learning; Domain (mathematical analysis); Majority rule; Quality (philosophy); Artificial intelligence; Data mining; Maximization; Domain knowledge; Data science; World Wide Web","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0009510909,0.0002176584,0.000317298,0.00007037856,0.0001803783,0.0003258254,0.001095463,0.00005831807,0.00007700568],"category_scores_gemma":[0.0000396123,0.0001657273,0.00005245583,0.0002478547,0.00004435685,0.0006053007,0.0005428214,0.00009002083,0.0001730566],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000629853,"about_ca_system_score_gemma":0.0001507957,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006423232,"about_ca_topic_score_gemma":0.00004131633,"domain_scores_codex":[0.9979749,0.00008643421,0.0003679384,0.0008571975,0.0002640812,0.0004494003],"domain_scores_gemma":[0.9965993,0.0003605112,0.0001288523,0.002654266,0.0001480292,0.0001090054],"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":[0.00004972727,0.0006838145,0.000555927,0.0005522292,0.0001738555,0.000008386799,0.00792277,0.0003622627,0.05673299,0.01918454,0.01922782,0.8945457],"study_design_scores_gemma":[0.001193357,0.0004084241,0.0002256848,0.0002240375,0.0000122612,0.00002770377,0.0006258854,0.9138635,0.03900874,0.0002014361,0.04358915,0.000619821],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2748771,0.0002323972,0.7177207,0.0001879007,0.0004413375,0.000808492,0.000002964932,0.0003859109,0.005343207],"genre_scores_gemma":[0.7969686,0.000003710117,0.2001322,0.0003024974,0.00007907875,0.00004453847,0.00001691052,0.0000202039,0.002432281],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9135013,"threshold_uncertainty_score":0.6758162,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05201715153195304,"score_gpt":0.3420977152560519,"score_spread":0.2900805637240988,"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."}}