{"id":"W4382322446","doi":"10.48550/arxiv.2306.14156","title":"Matching-based Hybrid Service Trading for Task Assignment over Dynamic Mobile Crowdsensing Networks","year":2023,"lang":"en","type":"preprint","venue":"arXiv (Cornell University)","topic":"Mobile Crowdsensing and Crowdsourcing","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"Basic and Applied Basic Research Foundation of Guangdong Province; Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China; Natural Science Foundation of Xiamen City","keywords":"Computer science; Matching (statistics); Task (project management); Service (business); Provisioning; Futures contract; Quality (philosophy); Mode (computer interface); Quality of service; Distributed computing; Computer security; Computer network; Human–computer interaction; Business; Finance","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":[0.002935319,0.0009757315,0.001555084,0.0007518464,0.001426639,0.001553677,0.003077312,0.001435575,0.002168479],"category_scores_gemma":[0.009476013,0.0004631264,0.0008285109,0.001209563,0.001318953,0.001997878,0.003322024,0.001074194,0.0003746506],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001496519,"about_ca_system_score_gemma":0.001862438,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004329629,"about_ca_topic_score_gemma":0.003225144,"domain_scores_codex":[0.9971235,0.0008513485,0.0001452542,0.000732733,0.0006404009,0.0005066583],"domain_scores_gemma":[0.9953949,0.002481472,0.0006061394,0.0006710395,0.0004135231,0.0004328761],"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":[0.0007894117,0.0002928931,0.002952074,0.0002124843,0.0001145586,0.00042742,0.000534856,0.8129149,0.01275487,0.06129742,0.002087298,0.1056218],"study_design_scores_gemma":[0.00001800333,0.00006307242,0.0001911812,0.000004627356,0.00001066724,0.00005079184,0.0000468344,0.9781519,0.0006998116,0.02016951,0.0005821695,0.00001143902],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06287091,0.0002353447,0.9333829,0.0002447625,0.00006605017,0.0001583375,0.00008736759,0.0004066667,0.002547706],"genre_scores_gemma":[0.9464448,0.0000943253,0.05158997,0.0000947794,0.00003894304,0.0001146543,0.00005591084,0.00003632724,0.001530332],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004329629,"threshold_uncertainty_score":0.01552361,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04750450707435529,"score_gpt":0.2019911266911436,"score_spread":0.1544866196167883,"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."}}