{"id":"W4406416517","doi":"10.1109/tce.2025.3529661","title":"Split Learning-Based Robust Resource Allocation for Consumer Electronics in Smart Cities","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Consumer Electronics","topic":"Smart Parking Systems Research","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"École de Technologie Supérieure","funders":"Fundamental Research Funds for the Key Research Program of Chongqing Science and Technology Commission","keywords":"Electronics; Computer science; Resource allocation; Engineering; Electronic engineering; Electrical engineering; Computer network","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.00065036,0.000363736,0.0004064448,0.0008280808,0.0002812402,0.0001035484,0.0003040688,0.0002979827,0.000048028],"category_scores_gemma":[0.00004364869,0.0004383668,0.0001749135,0.0009198276,0.0001090032,0.0001107371,0.000001630558,0.001264126,0.00004734699],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001164209,"about_ca_system_score_gemma":0.0006772363,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00007437815,"about_ca_topic_score_gemma":0.001617021,"domain_scores_codex":[0.9974143,0.0001789341,0.0005498563,0.0004712691,0.0003254753,0.001060202],"domain_scores_gemma":[0.9983874,0.0008294253,0.00005280625,0.0004866592,0.0001548666,0.00008886059],"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":[0.0002334344,0.0001295133,0.000384217,0.0003594483,0.0002918868,0.00000224355,0.0001097268,0.9776099,0.002648077,0.0005845063,0.002083969,0.01556311],"study_design_scores_gemma":[0.002164896,0.0002230875,0.00009987642,0.0002217754,0.0001104339,0.000006244582,0.00009678114,0.5870058,0.06022296,0.0001056596,0.3492103,0.0005321701],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08032244,0.007475858,0.9069868,0.0005062698,0.0006495221,0.001675592,0.00003626406,0.000842572,0.001504728],"genre_scores_gemma":[0.995082,0.0005732306,0.0004406377,0.00009643901,0.00002442305,0.0008729749,0.00003503381,0.0001054071,0.002769822],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9147596,"threshold_uncertainty_score":0.9998068,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01571088724623584,"score_gpt":0.2539366496161663,"score_spread":0.2382257623699305,"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."}}