{"id":"W4244909627","doi":"10.1145/3381519","title":"Peer Prediction with Heterogeneous Users","year":2020,"lang":"en","type":"article","venue":"ACM Transactions on Economics and Computation","topic":"Mobile Crowdsensing and Crowdsourcing","field":"Computer Science","cited_by":12,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"Harvard School of Engineering and Applied Sciences; Google; National Science Foundation","keywords":"Computer science; Cluster analysis; Incentive; Robustness (evolution); Homogeneous; Limiting; Mechanism (biology); Incentive compatibility; Data mining; Machine learning; Artificial intelligence; Mathematics; Microeconomics","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":[0.008978752,0.001082044,0.002353651,0.001032308,0.001909109,0.002555782,0.00414031,0.002781396,0.00471156],"category_scores_gemma":[0.04751226,0.0009221797,0.001064466,0.001583941,0.002084245,0.005382132,0.003332139,0.002601893,0.001260284],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001787797,"about_ca_system_score_gemma":0.00140541,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006238439,"about_ca_topic_score_gemma":0.003796038,"domain_scores_codex":[0.9924838,0.002600139,0.0003310649,0.002561331,0.001216931,0.000806693],"domain_scores_gemma":[0.9628303,0.02014627,0.004440601,0.008653538,0.002790585,0.001138716],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.001850744,0.0003156739,0.05018575,0.0003426391,0.0003639129,0.00186169,0.002374831,0.5080926,0.005536862,0.3121211,0.0121682,0.1047859],"study_design_scores_gemma":[0.00009563982,0.00007732809,0.001988747,0.00002094958,0.00004576646,0.0002156245,0.0002280454,0.8912234,0.001377206,0.1026254,0.002053885,0.00004803215],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.2504711,0.0005821669,0.7366086,0.002510473,0.0001847903,0.0003000913,0.0006075362,0.0008150294,0.007920268],"genre_scores_gemma":[0.9733059,0.0001198761,0.02245135,0.0001545824,0.00009510764,0.00009127174,0.0001797077,0.00003164227,0.0035705],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008978752,"threshold_uncertainty_score":0.04748476,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01830308227507904,"score_gpt":0.2082043424436197,"score_spread":0.1899012601685406,"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."}}