{"id":"W2290496639","doi":"","title":"A Statistical Analysis of the Aggregation of Crowdsourced Labels","year":2015,"lang":"en","type":"dissertation","venue":"UWSpace (University of Waterloo)","topic":"Mobile Crowdsensing and Crowdsourcing","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"University of Waterloo","keywords":"Crowdsourcing; Computer science; Focus (optics); Quality (philosophy); Task (project management); Property (philosophy); Measure (data warehouse); Machine learning; Artificial intelligence; Data mining; Data science; Engineering; 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003360415,0.000168477,0.0005943815,0.0004558929,0.000108465,0.00002024547,0.0008877806,0.000196034,0.00003191526],"category_scores_gemma":[0.00006631813,0.0001561846,0.0002806792,0.001213942,0.000151777,0.000139612,0.0001413761,0.000169886,0.00000314367],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005435547,"about_ca_system_score_gemma":0.0001832887,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01589523,"about_ca_topic_score_gemma":0.01451434,"domain_scores_codex":[0.9985468,0.0001535672,0.0002309349,0.0003239328,0.0005660812,0.0001786327],"domain_scores_gemma":[0.9978106,0.00009792834,0.0007084372,0.0007495895,0.0005606903,0.00007274401],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"observational","study_design_scores_codex":[0.0004527977,0.000565326,0.0120436,0.001620252,0.005488135,0.00005112707,0.8278045,0.01326829,0.05514033,0.01720358,0.005283887,0.06107816],"study_design_scores_gemma":[0.004538578,0.0009953388,0.3598081,0.002814417,0.0155162,0.00001222038,0.290184,0.2165126,0.1007359,0.005513932,0.0009559419,0.002412729],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.995733,0.00006166937,0.003343417,0.0001328249,0.0001810448,0.0001421219,0.00005759913,0.00002690148,0.0003214484],"genre_scores_gemma":[0.9519506,0.00001087484,0.00614658,0.000005719717,0.000009341578,1.633313e-7,0.000132962,0.00001075815,0.04173305],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5376205,"threshold_uncertainty_score":0.990658,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01043263202640088,"score_gpt":0.214734835149475,"score_spread":0.2043022031230741,"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."}}