{"id":"W4393185130","doi":"10.1109/jiot.2024.3381834","title":"Machine-Learning-Based Optimal Cooperating Node Selection for Internet of Underwater Things","year":2024,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Underwater Vehicles and Communication Systems","field":"Engineering","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Selection (genetic algorithm); The Internet; Underwater; Artificial intelligence; Node (physics); Machine learning; Computer network; World Wide Web; Engineering","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006921759,0.0001910133,0.0002998683,0.0002364013,0.0000496885,0.000258739,0.0004121111,0.0001142486,0.0000906053],"category_scores_gemma":[0.0000130562,0.0001657235,0.0002176499,0.0001250065,0.00003990358,0.0004896224,0.00003721971,0.0006786381,0.000009850117],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001213291,"about_ca_system_score_gemma":0.00003868173,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001951273,"about_ca_topic_score_gemma":0.000006862178,"domain_scores_codex":[0.998576,0.00007745625,0.0007374645,0.0001499342,0.0002297617,0.0002293256],"domain_scores_gemma":[0.9993097,0.0001358909,0.000173794,0.0001117294,0.0002005898,0.00006827715],"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":[0.0002722385,0.0001294278,0.002050164,0.002164847,0.00111477,0.00001249747,0.02370037,0.4385693,0.5133164,0.0005084408,0.004106699,0.0140548],"study_design_scores_gemma":[0.0002834708,0.0001648506,0.000005143721,0.0006493384,0.00003055554,0.0001125171,0.00009920177,0.7144372,0.2772298,0.00006425445,0.006800751,0.000122837],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1685853,0.0006759826,0.829293,0.0001571686,0.0005204562,0.0001266727,0.000003243873,0.0001793378,0.0004588043],"genre_scores_gemma":[0.9834672,0.00002906152,0.01552158,0.00008089514,0.0001227822,0.00001047556,0.000007877916,0.00006497759,0.0006951778],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8148819,"threshold_uncertainty_score":0.6758009,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01680129586172168,"score_gpt":0.243573147457859,"score_spread":0.2267718515961374,"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."}}