{"id":"W2295497133","doi":"10.1609/aaai.v26i1.8099","title":"Adaptive Polling for Information Aggregation","year":2021,"lang":"en","type":"article","venue":"Proceedings of the AAAI Conference on Artificial Intelligence","topic":"Mobile Crowdsensing and Crowdsourcing","field":"Computer Science","cited_by":68,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; Xerox Foundation; John Templeton Foundation; National Science Foundation","keywords":"Pairwise comparison; Polling; Ranking (information retrieval); Computer science; Simple (philosophy); Machine learning; Artificial intelligence; Data mining","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":[0.007834055,0.001440426,0.003399732,0.001633941,0.001982039,0.002106732,0.003975258,0.002886284,0.003211648],"category_scores_gemma":[0.03616661,0.0009844501,0.001297354,0.001973045,0.001823694,0.004215068,0.003393445,0.002736638,0.0006715261],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001710591,"about_ca_system_score_gemma":0.001712781,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00451396,"about_ca_topic_score_gemma":0.004989738,"domain_scores_codex":[0.9930485,0.003069075,0.0003364648,0.001918114,0.001154819,0.000473028],"domain_scores_gemma":[0.9712605,0.02060305,0.001363407,0.004997851,0.001120329,0.0006548305],"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":[0.00165532,0.001184472,0.01319139,0.0005651391,0.0006220562,0.0007616077,0.001929782,0.5552254,0.01567534,0.07636832,0.009815186,0.323006],"study_design_scores_gemma":[0.00007370394,0.00009907256,0.0006435346,0.00001534853,0.00003249355,0.00006975314,0.00006208648,0.9561716,0.0012947,0.04038746,0.001120902,0.00002929657],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06734321,0.000499461,0.926558,0.0005902906,0.0001507864,0.000385731,0.0002343886,0.001549816,0.002688237],"genre_scores_gemma":[0.8101611,0.0001185979,0.1856703,0.0004073522,0.0002003487,0.0004930786,0.0003405659,0.0001180474,0.00249057],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.007834055,"threshold_uncertainty_score":0.04143095,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06370963450995835,"score_gpt":0.2734499180590542,"score_spread":0.2097402835490958,"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."}}