{"id":"W7117547354","doi":"10.1109/jsac.2025.3649622","title":"On-Device Machine Learning Model Adaptation for 6G Communications: From Standardization to Over-the-Air Prototyping Experiments Based on Beam Prediction","year":2025,"lang":"","type":"article","venue":"IEEE Journal on Selected Areas in Communications","topic":"Advanced Neural Network Applications","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Qualcomm (Canada)","funders":"","keywords":"Standardization; Adaptation (eye); Benchmark (surveying); Inference; Wireless; Process (computing); Rapid prototyping; Data modeling","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":[0.001949436,0.0009287347,0.0005567027,0.0002757646,0.0002895904,0.000834122,0.0009960314,0.0007861287,0.002234143],"category_scores_gemma":[0.005315285,0.0002167374,0.0004373328,0.0002922771,0.0007263931,0.001073497,0.001193647,0.001396107,0.0005429887],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003451355,"about_ca_system_score_gemma":0.0003876917,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003570953,"about_ca_topic_score_gemma":0.002785044,"domain_scores_codex":[0.9986739,0.0004989622,0.00006677301,0.000203902,0.0003622927,0.000194175],"domain_scores_gemma":[0.9968226,0.002000207,0.000122126,0.0005895254,0.0003506657,0.0001148172],"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":[0.002040087,0.001501608,0.01008145,0.000582762,0.0002502664,0.001071908,0.0008788105,0.6444842,0.05971581,0.006609777,0.007582,0.2652014],"study_design_scores_gemma":[0.0001886359,0.001709467,0.005778705,0.00004417788,0.00004362082,0.0003469374,0.0003066775,0.9351353,0.04886324,0.002448225,0.005073136,0.00006186303],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.7868498,0.000595381,0.1970865,0.000699512,0.0004152273,0.0003381076,0.0003994266,0.002778079,0.0108379],"genre_scores_gemma":[0.9743663,0.0001203713,0.02372013,0.0001217325,0.00002126153,0.00007721687,0.0002204284,0.00008303302,0.001269568],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.003570953,"threshold_uncertainty_score":0.0103097,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05841679081063163,"score_gpt":0.3521063930030678,"score_spread":0.2936896021924362,"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."}}