{"id":"W4320029510","doi":"10.1109/globecom48099.2022.10000854","title":"Impact of Users' Mobility on the Quality of Edge Sensing Systems","year":2022,"lang":"en","type":"article","venue":"GLOBECOM 2022 - 2022 IEEE Global Communications Conference","topic":"Mobile Crowdsensing and Crowdsourcing","field":"Computer Science","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"Qatar University","keywords":"Waypoint; Incentive; Computer science; Enhanced Data Rates for GSM Evolution; Exploit; Scheme (mathematics); Computer network; Randomness; Mobility model; Quality (philosophy); Real-time computing; Computer security; Telecommunications","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":[0.003049769,0.0007102496,0.000674195,0.0007135569,0.0007341782,0.001247366,0.0009473875,0.0009743636,0.0009499382],"category_scores_gemma":[0.01415653,0.0002280563,0.0003644707,0.0009239431,0.001027592,0.00140525,0.001754405,0.0005459657,0.0001129716],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001505016,"about_ca_system_score_gemma":0.0007772179,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006018913,"about_ca_topic_score_gemma":0.00323818,"domain_scores_codex":[0.9965171,0.001585685,0.0001519286,0.0003371433,0.0006445693,0.0007635792],"domain_scores_gemma":[0.9883541,0.006946038,0.001603965,0.001031887,0.001566797,0.0004971199],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.001663931,0.0002893876,0.06291053,0.0001857858,0.0001920839,0.0007355846,0.0003695539,0.8649045,0.01742213,0.0111745,0.001085189,0.03906675],"study_design_scores_gemma":[0.0000797267,0.000849622,0.01964694,0.00003412869,0.000105778,0.0003872468,0.0004975068,0.9629781,0.01080167,0.003474575,0.001080892,0.00006382583],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9463387,0.0009344,0.04730341,0.0008456893,0.00005608956,0.00006673039,0.0001806722,0.0001810569,0.004093279],"genre_scores_gemma":[0.9991202,0.00005027078,0.0006745475,0.00001648052,0.000003730995,0.000004849411,0.00001129978,0.00000298763,0.0001157727],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006018913,"threshold_uncertainty_score":0.01612896,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08411425092889377,"score_gpt":0.3463369780461799,"score_spread":0.2622227271172861,"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."}}