{"id":"W2888319397","doi":"10.1109/jiot.2018.2866973","title":"Location-Dependent Task Allocation for Mobile Crowdsensing With Clustering Effect","year":2018,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Mobile Crowdsensing and Crowdsourcing","field":"Computer Science","cited_by":87,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of New Brunswick","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Cluster analysis; Task (project management); Profit (economics); Task analysis; Key (lock); Energy consumption; Data mining; Machine learning; Distributed computing","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":[],"consensus_categories":[],"category_scores_codex":[0.001249791,0.0001995981,0.0002693603,0.0001925044,0.0002168626,0.0004689517,0.0006346391,0.00007895777,0.000004752626],"category_scores_gemma":[0.00009592964,0.0001604441,0.00009977842,0.0001970087,0.0001209141,0.0007920603,0.00008578173,0.0002582716,0.00001148395],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000118143,"about_ca_system_score_gemma":0.0000964244,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007336669,"about_ca_topic_score_gemma":0.00001761353,"domain_scores_codex":[0.9983885,0.0000913366,0.0004631362,0.0003300189,0.0003918117,0.0003352196],"domain_scores_gemma":[0.9980696,0.0002008984,0.0004931387,0.0003784786,0.0007393603,0.0001185495],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001187346,0.0003426838,0.001603067,0.0008877967,0.0007295341,0.0001107454,0.04603354,0.03290832,0.5332348,0.001196271,0.005983568,0.3757823],"study_design_scores_gemma":[0.001297992,0.002698712,0.0001143936,0.001366872,0.00005513561,0.0019438,0.0001526342,0.5085915,0.4822646,0.0003559029,0.0007995887,0.0003588414],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3195532,0.00006398639,0.6788619,0.00008986151,0.0009902176,0.0001920006,1.917966e-7,0.00005540511,0.0001932699],"genre_scores_gemma":[0.9533514,0.000002863931,0.04570813,0.000181511,0.0004327253,0.00001087901,5.812092e-7,0.00002471893,0.0002872129],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6337982,"threshold_uncertainty_score":0.654272,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008561934544858405,"score_gpt":0.2465666502765096,"score_spread":0.2380047157316512,"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."}}