{"id":"W3006730595","doi":"10.23919/ifipnetworking46909.2019.8999406","title":"EFusion: correcting unreliable labels with expert knowledge for mobile crowdsensing","year":2019,"lang":"en","type":"article","venue":"","topic":"Mobile Crowdsensing and Crowdsourcing","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University; University of Victoria","funders":"","keywords":"Crowdsensing; Computer science; Crowd sourcing; Mobile device; Big data; Crowdsourcing; Mobile computing; Artificial intelligence; Human–computer interaction; Data science; Computer security; World Wide Web; Data mining; Telecommunications","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":[0.0004579082,0.0002495514,0.0003097482,0.0001128371,0.0003556525,0.0003647936,0.0004340348,0.00009503868,0.00005178209],"category_scores_gemma":[0.00004316992,0.0001954066,0.00009022487,0.000468489,0.0000382553,0.0004197752,0.0002066468,0.0001524316,0.0001722646],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006917992,"about_ca_system_score_gemma":0.0001305315,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000750879,"about_ca_topic_score_gemma":0.0000261289,"domain_scores_codex":[0.998202,0.00004568767,0.0002683175,0.0006965775,0.0002100201,0.0005774475],"domain_scores_gemma":[0.9981484,0.0005082243,0.00010526,0.000831015,0.0002746019,0.0001325423],"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":[0.0001888091,0.0006420179,0.003767097,0.0003914096,0.0001620534,0.0000501389,0.0189497,0.01402023,0.1313352,0.01925178,0.04076615,0.7704754],"study_design_scores_gemma":[0.003142612,0.001421382,0.0001340824,0.0008960727,0.00002285265,0.0004655196,0.002702658,0.5812643,0.2527523,0.0004072727,0.1552898,0.001501131],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4046998,0.0004751396,0.5633654,0.0001560687,0.002018954,0.0009637646,7.801093e-7,0.0007514593,0.02756866],"genre_scores_gemma":[0.8915024,0.000004739358,0.09620174,0.0004009659,0.0001724448,0.00004857716,0.000002204437,0.0000383117,0.01162855],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7689742,"threshold_uncertainty_score":0.7968449,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009118071700390563,"score_gpt":0.247601415149676,"score_spread":0.2384833434492854,"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."}}