{"id":"W4410335224","doi":"10.1016/j.ins.2025.122290","title":"TrueCome: Effective data truth discovery based on fuzzy clustering with prior constraints","year":2025,"lang":"en","type":"article","venue":"Information Sciences","topic":"Mobile Crowdsensing and Crowdsourcing","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Alberta","funders":"Higher Education Discipline Innovation Project; Xidian University; Natural Science Foundation of Shaanxi Province; National Natural Science Foundation of China","keywords":"Computer science; Cluster analysis; Fuzzy clustering; Data mining; Fuzzy logic; Artificial intelligence","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":[0.008102102,0.001835006,0.00404108,0.004442779,0.0026863,0.004986434,0.00799402,0.004564593,0.005383702],"category_scores_gemma":[0.03595098,0.001893131,0.002477109,0.004092218,0.003548303,0.009706322,0.009659915,0.005368312,0.001553002],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0023608,"about_ca_system_score_gemma":0.004331907,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01028586,"about_ca_topic_score_gemma":0.01220146,"domain_scores_codex":[0.9935012,0.001778767,0.000311875,0.001893623,0.002122225,0.0003922955],"domain_scores_gemma":[0.9769604,0.0153728,0.001106208,0.003741926,0.002078171,0.0007405906],"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":[0.002273674,0.0004877247,0.002320653,0.000881233,0.0004952343,0.0005817206,0.0008581431,0.37719,0.007908447,0.1558654,0.021738,0.4293999],"study_design_scores_gemma":[0.00005774586,0.00004858406,0.0001485225,0.00003505264,0.00003064366,0.00009721025,0.00006019607,0.9175534,0.001956599,0.07805178,0.001927145,0.00003307201],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.003618001,0.0003067681,0.9935775,0.0004143091,0.00008235443,0.00009685972,0.0003214889,0.0009646015,0.0006180772],"genre_scores_gemma":[0.1849346,0.0005619375,0.8070449,0.0004986729,0.0004762579,0.0003289276,0.002058265,0.0004862227,0.003610262],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01028586,"threshold_uncertainty_score":0.04284853,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01587118895600554,"score_gpt":0.2709001222210193,"score_spread":0.2550289332650137,"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."}}