{"id":"W4412722439","doi":"10.1109/tcomm.2025.3593643","title":"Cross-Modal Semantic Transmission Strategy for Mobile Scenarios","year":2025,"lang":"en","type":"article","venue":"IEEE Transactions on Communications","topic":"IPv6, Mobility, Handover, Networks, Security","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"Qinglan Project of Jiangsu Province of China; National Natural Science Foundation of China","keywords":"Computer science; Modal; Transmission (telecommunications); Mobile telephony; Electronic engineering; Computer network; Telecommunications; Mobile radio; Engineering; Materials science","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":[0.0006395333,0.0007217458,0.000468655,0.0005389738,0.0004981247,0.0006168851,0.0009236865,0.0008133406,0.002100005],"category_scores_gemma":[0.001404552,0.0002412231,0.0005530872,0.0004102198,0.0006369395,0.001713963,0.001695799,0.0007902801,0.0004496115],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004142417,"about_ca_system_score_gemma":0.0005337788,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0009569369,"about_ca_topic_score_gemma":0.000855599,"domain_scores_codex":[0.9996984,0.00009159968,0.00001945143,0.0000515409,0.0001009833,0.00003813487],"domain_scores_gemma":[0.9995916,0.0001604316,0.00004709328,0.00005562026,0.0001124224,0.00003281824],"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":[0.0002683667,0.00009702344,0.000669241,0.0002321913,0.00007342338,0.0005987377,0.0004663443,0.6160027,0.05634343,0.1370833,0.00294018,0.185225],"study_design_scores_gemma":[0.000008856799,0.00007366213,0.00007517511,0.000007556631,0.00001209156,0.0001552812,0.00004885048,0.9794452,0.004226218,0.01445557,0.001477356,0.00001413039],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.009549082,0.0001308903,0.9875665,0.00008794577,0.00002473207,0.00001992048,0.00001710616,0.0001447539,0.002459018],"genre_scores_gemma":[0.7770377,0.000348021,0.2173385,0.0001966155,0.00004909088,0.000112243,0.0001514527,0.00008442977,0.004681766],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002100005,"threshold_uncertainty_score":0.007025182,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02334010217800461,"score_gpt":0.3099964418576507,"score_spread":0.2866563396796461,"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."}}