{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003267985,0.0001533377,0.0001798442,0.0001158685,0.0001725448,0.0001932489,0.0002594048,0.00008320065,0.00003710881],"category_scores_gemma":[0.00003836874,0.0001451839,0.0001260141,0.0002556761,0.00002029899,0.000369126,0.00008250906,0.00009337093,0.0001365503],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008182045,"about_ca_system_score_gemma":0.00007568804,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005044048,"about_ca_topic_score_gemma":0.000007158478,"domain_scores_codex":[0.9987137,0.00004684521,0.0002036858,0.0005010999,0.000199108,0.0003355407],"domain_scores_gemma":[0.9988751,0.0001380165,0.00009498725,0.0005904299,0.0002230606,0.00007842149],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000148126,0.0003987574,0.000479997,0.0003403355,0.0001147019,0.00003195718,0.002662139,0.01824356,0.3502388,0.5226757,0.01081994,0.09384605],"study_design_scores_gemma":[0.0009121175,0.0002204131,0.00004434994,0.00007173522,0.00001128625,0.00001356495,0.0002535043,0.8448765,0.1422596,0.005036475,0.005976962,0.0003235431],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1156785,0.000007823761,0.8804238,0.0002734044,0.0009525418,0.0006376407,0.000001455966,0.0003352257,0.001689677],"genre_scores_gemma":[0.8753908,8.521922e-7,0.1216238,0.0009560788,0.00006540987,0.00002962771,0.000003182605,0.00001578488,0.001914381],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8266329,"threshold_uncertainty_score":0.5920427,"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."}}