{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006376669,0.001305597,0.001831398,0.00227705,0.0009311102,0.001740888,0.002655147,0.001961959,0.001306881],"category_scores_gemma":[0.02281499,0.0006567474,0.001041359,0.001401526,0.001637935,0.002704931,0.004282243,0.001813155,0.0006846537],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001252785,"about_ca_system_score_gemma":0.001488573,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002797509,"about_ca_topic_score_gemma":0.003065484,"domain_scores_codex":[0.9955663,0.00112537,0.0002611765,0.001256738,0.00145411,0.0003363081],"domain_scores_gemma":[0.9874802,0.00650597,0.001239863,0.001722865,0.002693369,0.0003576687],"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":[0.001009045,0.0004229838,0.007998592,0.0005573911,0.0001657987,0.0005142723,0.0009369614,0.2494778,0.06062851,0.00827786,0.007026233,0.6629846],"study_design_scores_gemma":[0.00006319194,0.000100545,0.001596332,0.00002907928,0.00002576867,0.0001562379,0.0001208387,0.965865,0.01672349,0.01310909,0.00216884,0.00004153994],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01626736,0.0002450348,0.9807495,0.0002643683,0.00004473567,0.0001056027,0.00009761416,0.001333215,0.0008924503],"genre_scores_gemma":[0.5752634,0.0002602883,0.4217621,0.0004024863,0.0001393939,0.0001682134,0.0005132594,0.0002154344,0.001275369],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006376669,"threshold_uncertainty_score":0.03372347,"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."}}