{"id":"W4413267664","doi":"10.1109/jiot.2025.3593247","title":"CaMPASS-Net: A Deep Learning Framework on Capacity Maximization for MIMO Pinching Antenna Systems in IoT","year":2025,"lang":"","type":"article","venue":"IEEE Internet of Things Journal","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"National Research Foundation of Korea","keywords":"MIMO; Computer science; Maximization; 3G MIMO; Antenna (radio); Internet of Things; Net (polyhedron); Multi-user MIMO; Telecommunications; Mathematical optimization; Computer security; Mathematics; Beamforming","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","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.001834512,0.0006345015,0.001139235,0.001375514,0.0002220664,0.0004514483,0.0006866766,0.0006997833,0.00003010272],"category_scores_gemma":[0.002548284,0.0007012939,0.0003519672,0.0007962039,0.00008064773,0.0007787258,0.00006929309,0.002794062,0.00000793936],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001477519,"about_ca_system_score_gemma":0.00009081975,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003510681,"about_ca_topic_score_gemma":0.0000283213,"domain_scores_codex":[0.995423,0.0003757831,0.002373287,0.0005524337,0.0004628249,0.0008126252],"domain_scores_gemma":[0.9965048,0.0009665097,0.001443514,0.000365405,0.0005700144,0.0001497385],"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":[0.0003356815,0.0001156609,0.001272524,0.001562501,0.0003333808,0.00001924437,0.01010804,0.9749459,0.004711223,0.002227513,0.0001392412,0.004229083],"study_design_scores_gemma":[0.001250347,0.0003543186,0.0001247975,0.02409841,0.0001070491,0.000120511,0.001323187,0.9670275,0.002325684,0.002434071,0.0003226409,0.0005114237],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06929715,0.002759326,0.9167101,0.0001127544,0.009661592,0.0009376888,0.000007754466,0.0001028333,0.000410808],"genre_scores_gemma":[0.9546976,0.000359343,0.04363083,0.00008499836,0.0005299439,0.00003402193,0.000005126493,0.0001342931,0.0005238492],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8854005,"threshold_uncertainty_score":0.9995438,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01272837942162846,"score_gpt":0.2521458349467365,"score_spread":0.2394174555251081,"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."}}