{"id":"W4417282725","doi":"10.1109/pimrc62392.2025.11275091","title":"Hierarchical Feature Encoding in 6G in-X Subnetworks Using Information Bottleneck","year":2025,"lang":"","type":"article","venue":"","topic":"Advanced Wireless Communication Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Inference; Information bottleneck method; Bottleneck; Redundancy (engineering); Robustness (evolution); Mutual information; Encoding (memory); Channel (broadcasting); Feature (linguistics); Task (project management)","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":[0.0003118411,0.0003162361,0.0004151643,0.001363748,0.0001021796,0.0001125263,0.0007213001,0.0006973101,0.00003554566],"category_scores_gemma":[0.0003166485,0.0003669311,0.00006518325,0.002496862,0.0001211492,0.00137185,0.0005594949,0.00195008,0.00001275213],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008427167,"about_ca_system_score_gemma":0.00009380405,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006923234,"about_ca_topic_score_gemma":0.0002659071,"domain_scores_codex":[0.9981635,0.00007204647,0.0008138563,0.0002188367,0.0001635019,0.0005683169],"domain_scores_gemma":[0.9986675,0.0003149408,0.00009764641,0.000829055,0.00005189762,0.00003901032],"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":[0.00001977414,0.00002891721,0.008108573,0.0001740484,0.00001607182,0.00000315722,0.0004350742,0.8264602,0.0004873337,0.04483253,0.0001221427,0.1193122],"study_design_scores_gemma":[0.0007734706,0.00001163039,0.008173648,0.001089215,0.000006219002,0.000002771887,0.001284389,0.9760522,0.002496228,0.005929256,0.003798277,0.0003826581],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2002437,0.006581933,0.7560999,0.00292305,0.0008124132,0.0008917285,0.000006384886,0.0009627574,0.03147808],"genre_scores_gemma":[0.9520686,0.003620757,0.04400108,0.0001252413,0.00001607483,0.00003280015,0.00001462344,0.00001827503,0.0001025763],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7518249,"threshold_uncertainty_score":0.9998783,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01166764347050723,"score_gpt":0.2627110575792777,"score_spread":0.2510434141087704,"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."}}