{"id":"W4409048322","doi":"10.1109/tai.2025.3556375","title":"TSTNet: Temporal Semantic Transformer-Based Computing Power Network for Automatic Driving in the Internet of Vehicles","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Artificial Intelligence","topic":"Graph Theory and Algorithms","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"","keywords":"Computer science; Transformer; The Internet; Power network; Computer network; Real-time computing; World Wide Web; Electrical engineering; Power (physics); Engineering; Electric power system","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":[0.0009811971,0.0001697518,0.0002305237,0.0002526435,0.000201905,0.0001008773,0.0008441216,0.00007120504,0.00001367711],"category_scores_gemma":[0.00001291127,0.0001392588,0.0001915846,0.001092979,0.0001388924,0.0001544251,0.000002001277,0.0002480956,0.000007045899],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002586311,"about_ca_system_score_gemma":0.00007213508,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005553408,"about_ca_topic_score_gemma":0.0002310041,"domain_scores_codex":[0.9984014,0.000175943,0.0005958322,0.0003123428,0.0001885559,0.0003259285],"domain_scores_gemma":[0.9984659,0.0009909041,0.00009622969,0.0003543203,0.00006133231,0.00003131868],"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":[0.00006278363,0.0006741213,0.0001053629,0.0001149306,0.0000522863,0.000007223276,0.004514793,0.3185716,0.0009859084,0.08219418,0.00004269367,0.5926741],"study_design_scores_gemma":[0.00006095641,0.0001732838,0.0001246023,0.0002933367,0.00001507861,0.000001836356,0.0003220885,0.8899426,0.07228538,0.03662128,0.00002925854,0.0001303294],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0760593,0.000024145,0.9219422,0.0006260157,0.0007323978,0.0004512205,0.000003234218,0.00007792483,0.00008357166],"genre_scores_gemma":[0.983147,0.000001735041,0.01646599,0.0003067149,0.000015587,0.00003588898,7.489818e-7,0.000007530905,0.00001874938],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9070877,"threshold_uncertainty_score":0.5678809,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02636390575376176,"score_gpt":0.2882913611853399,"score_spread":0.2619274554315781,"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."}}