{"id":"W2905052402","doi":"10.1109/tvt.2018.2885403","title":"Improved Recruitment Algorithms for Vehicular Crowdsensing Networks","year":2018,"lang":"en","type":"article","venue":"IEEE Transactions on Vehicular Technology","topic":"Mobile Crowdsensing and Crowdsourcing","field":"Computer Science","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"Lakehead University; Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Crowdsensing; Heuristics; Computer science; Algorithm; Approximation algorithm; Integer (computer science)","routes":{"ca_aff":true,"ca_fund":true,"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":[0.004545445,0.001996383,0.002229211,0.001494237,0.001773686,0.002255146,0.004544412,0.002971534,0.00595277],"category_scores_gemma":[0.01289071,0.0009916357,0.001440023,0.002001529,0.001327345,0.003011052,0.003751122,0.002455154,0.00188045],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002461983,"about_ca_system_score_gemma":0.00362503,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003884919,"about_ca_topic_score_gemma":0.004386302,"domain_scores_codex":[0.9968494,0.001336884,0.0001760751,0.0005735749,0.0006538628,0.0004102937],"domain_scores_gemma":[0.9947801,0.003234642,0.0004336293,0.0003930853,0.0007926067,0.0003659777],"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":[0.0002197957,0.0002365758,0.0006949471,0.0002148567,0.00004098368,0.00013526,0.0002701268,0.8284746,0.002386796,0.04686165,0.008600463,0.111864],"study_design_scores_gemma":[0.00003959929,0.00005384759,0.00007718373,0.00001938974,0.000008788667,0.00003973309,0.0000449506,0.9787396,0.0005166362,0.01727014,0.003176441,0.00001379076],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007498028,0.0005625549,0.9855682,0.0005930119,0.0001657591,0.0002187002,0.0000832538,0.0005848852,0.004725557],"genre_scores_gemma":[0.2882381,0.001013673,0.6941428,0.0009484109,0.0003176274,0.001252965,0.0005885898,0.0003649574,0.01313296],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.00595277,"threshold_uncertainty_score":0.02403891,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03046013407753191,"score_gpt":0.2745661948863181,"score_spread":0.2441060608087862,"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."}}