{"id":"W4384659592","doi":"10.1145/3539618.3592007","title":"Learning from Crowds with Annotation Reliability","year":2023,"lang":"en","type":"article","venue":"","topic":"Mobile Crowdsensing and Crowdsourcing","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Key Research and Development Program of China; Canadian Institute for Advanced Research","keywords":"Crowds; Computer science; Annotation; Crowdsourcing; Reliability (semiconductor); Quality (philosophy); Artificial intelligence; Machine learning; Supervised learning; World Wide Web","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01058565,0.002857635,0.002518326,0.002855308,0.001920489,0.00283569,0.002944663,0.002643612,0.001394977],"category_scores_gemma":[0.04690918,0.001325324,0.001608982,0.002152065,0.003718547,0.004655696,0.006183453,0.003920808,0.001164226],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002681531,"about_ca_system_score_gemma":0.002701668,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009437507,"about_ca_topic_score_gemma":0.007773998,"domain_scores_codex":[0.9898177,0.004244366,0.0004104362,0.003310073,0.0017214,0.0004960667],"domain_scores_gemma":[0.9633051,0.02430497,0.002805125,0.00475739,0.003849695,0.0009778274],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008716155,0.000240037,0.01797612,0.0005664802,0.0003287308,0.0004910391,0.002045578,0.6733188,0.006444087,0.01703668,0.01028302,0.2703979],"study_design_scores_gemma":[0.00002918405,0.00004975275,0.001146379,0.0000413997,0.0000364455,0.00005463484,0.0001147217,0.9574414,0.002470337,0.0369087,0.00166933,0.00003760495],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06861731,0.0008468293,0.9228959,0.00123858,0.0001547678,0.0001968057,0.0004177706,0.002582202,0.003049798],"genre_scores_gemma":[0.8117641,0.0004182569,0.1797092,0.0006638595,0.0004146288,0.0003554214,0.00140729,0.0005319691,0.004735354],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01058565,"threshold_uncertainty_score":0.05598295,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01142408964575587,"score_gpt":0.2164370281660434,"score_spread":0.2050129385202875,"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."}}