{"id":"W4408200778","doi":"10.1080/02533839.2025.2468724","title":"MS-ExTdO: mobile sink path planning based on extended Tasmanian devil optimization for wireless sensor networks","year":2025,"lang":"en","type":"article","venue":"Journal of the Chinese Institute of Engineers","topic":"Energy Efficient Wireless Sensor Networks","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"Horizon College and Seminary","funders":"","keywords":"Wireless sensor network; Sink (geography); Computer science; Computer network; Path (computing); Motion planning; Wireless; Telecommunications; Geography; Artificial intelligence","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":[0.0004943713,0.0002667924,0.0004249099,0.0003437661,0.000167833,0.00008273412,0.001197938,0.0001368132,0.00000222598],"category_scores_gemma":[0.0001987441,0.0001795435,0.0003205401,0.001062041,0.00007416741,0.0003288137,0.0000970354,0.0003692608,1.887527e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001299352,"about_ca_system_score_gemma":0.000176113,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003577427,"about_ca_topic_score_gemma":0.000001870642,"domain_scores_codex":[0.9983752,0.00006440381,0.0006453046,0.0002306387,0.0003874612,0.0002969797],"domain_scores_gemma":[0.9981621,0.0003020013,0.0005549933,0.0005540754,0.0003359942,0.00009087291],"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":[0.00007516658,0.000116366,0.0001814273,0.00004393829,0.00006020562,0.00001301493,0.00008558657,0.994947,0.0001537021,0.001700249,0.0006439661,0.001979332],"study_design_scores_gemma":[0.001038792,0.0001464248,0.0005767606,0.0006775744,0.00003457847,0.00001481766,0.00001342153,0.9959072,0.0003088861,0.0000369623,0.001080846,0.0001637254],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08148203,0.0002642271,0.9132855,0.000469337,0.003898416,0.0002711733,0.000003278333,0.00004547417,0.0002805647],"genre_scores_gemma":[0.9083189,0.00002145905,0.09108251,0.0002204307,0.0002405143,0.00001006504,0.000004318184,0.00002003125,0.00008178915],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8268368,"threshold_uncertainty_score":0.732157,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005524598849592699,"score_gpt":0.2325865764757176,"score_spread":0.2270619776261249,"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."}}