{"id":"W3156550766","doi":"10.1109/tgcn.2021.3074466","title":"Role Assignment for Spatially-Correlated Data Aggregation Using Multi-Sink Internet of Underwater Things","year":2021,"lang":"en","type":"article","venue":"IEEE Transactions on Green Communications and Networking","topic":"Underwater Vehicles and Communication Systems","field":"Engineering","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"Memorial University of Newfoundland","funders":"Equinor; National Science Foundation","keywords":"Upload; Computer science; Underwater; Data aggregator; Optimization problem; Raw data; Uncorrelated; Ant colony optimization algorithms; Sink (geography); Data collection; Energy consumption; Data mining; Mathematical optimization; Real-time computing; Algorithm; Computer network; Wireless sensor network; Engineering; Mathematics","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":[0.0007973216,0.0005994667,0.0006219127,0.0003575679,0.0005870613,0.0006544325,0.00117085,0.0005494546,0.0006893395],"category_scores_gemma":[0.001540119,0.0003193339,0.0005638207,0.000572552,0.0005671789,0.001285897,0.001103556,0.0005588692,0.000107115],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004938007,"about_ca_system_score_gemma":0.0005830728,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001529578,"about_ca_topic_score_gemma":0.002546601,"domain_scores_codex":[0.9994282,0.0002151807,0.00002704679,0.0001148313,0.000141431,0.00007329201],"domain_scores_gemma":[0.9994792,0.0002518866,0.00008319085,0.0000682418,0.00007851206,0.00003897909],"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":[0.0001358793,0.0001160489,0.001542223,0.0001347884,0.00006785724,0.0005028481,0.0002251998,0.9007665,0.01658307,0.03150865,0.001097831,0.04731903],"study_design_scores_gemma":[0.000006211121,0.00003655666,0.0001873309,0.000003325084,0.00001264781,0.00005562701,0.00004862283,0.9931079,0.001312734,0.0046573,0.0005644525,0.000007252394],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03736455,0.000191789,0.9602195,0.0001694415,0.00005961909,0.00005038927,0.00002389851,0.00008682126,0.001834044],"genre_scores_gemma":[0.8732908,0.0002086125,0.1243987,0.0001015224,0.00003491465,0.00009451749,0.00005865378,0.00003100219,0.001781374],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001529578,"threshold_uncertainty_score":0.004216671,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.089244309294564,"score_gpt":0.2786521665601864,"score_spread":0.1894078572656224,"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."}}