{"id":"W4402265330","doi":"10.1109/tmlcn.2024.3455268","title":"RSMA-Enabled Interference Management for Industrial Internet of Things Networks With Finite Blocklength Coding and Hardware Impairments","year":2024,"lang":"en","type":"article","venue":"IEEE Transactions on Machine Learning in Communications and Networking","topic":"Advanced Wireless Communication Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"Science and Engineering Research Council","keywords":"Internet of Things; Coding (social sciences); Computer science; Interference (communication); Industrial Internet; Computer hardware; The Internet; Computer network; Telecommunications; Embedded system; Operating system; Sociology; Channel (broadcasting)","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":[0.0004597339,0.0004182681,0.0003675983,0.0002329012,0.0003639811,0.0004417908,0.0006886474,0.0003329409,0.0005828289],"category_scores_gemma":[0.000840605,0.0001642154,0.0003247908,0.0002989296,0.0005994181,0.0005284572,0.0005927433,0.0005873714,0.0001218897],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006690606,"about_ca_system_score_gemma":0.0007334317,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004222022,"about_ca_topic_score_gemma":0.005126863,"domain_scores_codex":[0.9997432,0.00006975385,0.000009174581,0.0000448363,0.00008296749,0.00005013225],"domain_scores_gemma":[0.9996388,0.000165694,0.00007609667,0.00003081058,0.00006263924,0.0000258737],"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":[0.00004372558,0.00003936967,0.0004345842,0.00003870903,0.00002264424,0.00009417976,0.00005013765,0.9622222,0.00553308,0.007705499,0.0004320926,0.02338386],"study_design_scores_gemma":[0.000002342433,0.00001920032,0.00005844534,0.000001518348,0.000003389239,0.00001173032,0.000006003947,0.9978878,0.0005428568,0.001309426,0.0001550908,0.000002215505],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07189748,0.0004918552,0.9205217,0.000259322,0.00005161984,0.00004761614,0.00002530348,0.0003465666,0.006358335],"genre_scores_gemma":[0.977115,0.0001306618,0.02177219,0.00005403066,0.00001187248,0.00002943005,0.00001549354,0.00001146791,0.0008597365],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.004222022,"threshold_uncertainty_score":0.008394897,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02635729181548757,"score_gpt":0.2502221082225772,"score_spread":0.2238648164070896,"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."}}