{"id":"W2767317098","doi":"10.1186/s13638-017-0959-3","title":"A cognitive control approach to interference mitigation in communications-based train control (CBTC) co-existing with passenger information systems (PISs)","year":2017,"lang":"en","type":"article","venue":"EURASIP Journal on Wireless Communications and Networking","topic":"Advanced MIMO Systems Optimization","field":"Engineering","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Coastal Response Research Center, University of New Hampshire; Natural Science Foundation of Beijing Municipality; National Natural Science Foundation of China; China Railway","keywords":"Computer science; Interference (communication); Control (management); Cognition; Automotive engineering; Computer network; Artificial intelligence","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":[],"consensus_categories":[],"category_scores_codex":[0.0009755156,0.000257201,0.0003918115,0.0002976952,0.001147748,0.0007217072,0.0009205927,0.0001056347,7.01529e-7],"category_scores_gemma":[0.00008663225,0.0002439556,0.00004061213,0.000204027,0.0001760095,0.000910289,0.00006284883,0.0007534568,0.000004492042],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001909051,"about_ca_system_score_gemma":0.00004552457,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002561807,"about_ca_topic_score_gemma":0.00006763267,"domain_scores_codex":[0.9982294,0.0004009257,0.000722263,0.0001513115,0.0001903737,0.0003057463],"domain_scores_gemma":[0.9970701,0.0007188418,0.0005707596,0.001200459,0.0002900987,0.0001497315],"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":[0.0003635582,0.0002095036,0.02178067,0.0001930581,0.0002149498,0.000005218127,0.003469386,0.8588977,0.000485325,0.003403386,0.0000570924,0.1109201],"study_design_scores_gemma":[0.0026705,0.000109934,0.003336464,0.002053014,0.00003553139,0.00004652925,0.0009118255,0.9896744,0.00001561835,0.00001306873,0.0008319882,0.0003011113],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03068325,0.001268952,0.9577535,0.0005131981,0.0001903849,0.001281737,0.00003931124,0.0001397616,0.008129846],"genre_scores_gemma":[0.9941401,0.0005875024,0.00468051,0.0001368041,0.00009096623,0.0002496711,0.00006914334,0.00003965379,0.000005688257],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9634568,"threshold_uncertainty_score":0.994822,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03855560559846407,"score_gpt":0.2846467406613133,"score_spread":0.2460911350628492,"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."}}