{"id":"W2958890019","doi":"10.1109/icc.2019.8762030","title":"A Reverse Auction Based Incentive Mechanism for Mobile Crowdsensing","year":2019,"lang":"en","type":"article","venue":"","topic":"Mobile Crowdsensing and Crowdsourcing","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"","keywords":"Incentive; Computer science; Reverse auction; Mechanism design; Mechanism (biology); Process (computing); Work (physics); Lottery; Order (exchange); Computer security; Operations research; Common value auction; Business; Microeconomics; Operating system; Economics; Finance; Engineering","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":[0.006850503,0.001203442,0.001942758,0.001204466,0.001261737,0.002086964,0.004837491,0.002887086,0.004097273],"category_scores_gemma":[0.01038302,0.0009217572,0.001749236,0.001225956,0.001388252,0.003524174,0.002644792,0.002298327,0.0009612647],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001203828,"about_ca_system_score_gemma":0.002548914,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009265918,"about_ca_topic_score_gemma":0.0006997665,"domain_scores_codex":[0.9941028,0.002666322,0.0004135907,0.0009197296,0.00133258,0.0005649286],"domain_scores_gemma":[0.9950348,0.002234815,0.0007361719,0.0006174886,0.000932861,0.0004439933],"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":[0.001281109,0.001394711,0.001931123,0.001520689,0.0003240206,0.001438225,0.0006449846,0.3853977,0.03991641,0.2941632,0.009475663,0.2625121],"study_design_scores_gemma":[0.0002846425,0.0006115747,0.0002882509,0.00005753478,0.00008255676,0.0008400945,0.00006185682,0.9226058,0.004317295,0.05867307,0.01206246,0.0001147831],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.008786557,0.0003726469,0.9865192,0.0003293151,0.0001535986,0.0004358662,0.00006282668,0.0003893963,0.002950668],"genre_scores_gemma":[0.5717741,0.000513797,0.4173754,0.0004132747,0.0002104015,0.0009756782,0.0001140249,0.00009114694,0.00853225],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006850503,"threshold_uncertainty_score":0.03622931,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007958430239064056,"score_gpt":0.2229825007408865,"score_spread":0.2150240705018225,"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."}}