{"id":"W3197382768","doi":"10.1109/tgcn.2021.3109740","title":"Efficient Data Uploading for Mobile Crowdsensing via Team Collaborating and Matching","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Green Communications and Networking","topic":"Mobile Crowdsensing and Crowdsourcing","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"Natural Sciences and Engineering Research Council of Canada; New Brunswick Innovation Foundation","keywords":"Upload; Computer science; Enhanced Data Rates for GSM Evolution; Exploit; Crowdsensing; Matching (statistics); Mobile device; Edge computing; Lagrangian relaxation; Mobile edge computing; Distributed computing; Computer network; Computer security; Artificial intelligence; World Wide Web","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":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0006869755,0.0001950881,0.0002421024,0.0001098061,0.002174499,0.000514927,0.0006835742,0.00008460758,0.000001104097],"category_scores_gemma":[0.00000276491,0.0002159057,0.00004770868,0.0005656927,0.0001185873,0.0002149731,0.0001090794,0.00032496,0.000001133047],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004304553,"about_ca_system_score_gemma":0.00007673006,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000119506,"about_ca_topic_score_gemma":0.0003041874,"domain_scores_codex":[0.9984097,0.0001709453,0.0003570726,0.000582107,0.0001497532,0.0003304115],"domain_scores_gemma":[0.996406,0.0009314964,0.000129977,0.002261897,0.0001569785,0.0001136944],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000007106331,0.0001004921,0.00002417193,0.00004097162,0.00005687434,0.000003402173,0.001724788,0.01033339,0.005721508,0.0001936331,0.00003969729,0.9817539],"study_design_scores_gemma":[0.0003099611,0.00004397258,0.000007209421,0.0002582411,0.0000451423,0.0001064905,0.0004409564,0.992804,0.001061843,0.0002602128,0.004421721,0.0002402868],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0254912,0.002782942,0.970133,0.0006119696,0.0003296317,0.0002995623,0.00001752157,0.0001632023,0.0001709603],"genre_scores_gemma":[0.873991,0.000435447,0.12515,0.0002176389,0.00008015862,0.00003747554,0.00001122056,0.00002432401,0.00005276445],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9824706,"threshold_uncertainty_score":0.9991245,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04219220519627047,"score_gpt":0.287217167018701,"score_spread":0.2450249618224306,"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."}}