{"id":"W2024851746","doi":"10.1109/sahcn.2013.6645003","title":"Interference Aware Adaptive Clear Channel Assessment for improving ZigBee packet transmission under Wi-Fi interference","year":2013,"lang":"en","type":"article","venue":"","topic":"Wireless Networks and Protocols","field":"Computer Science","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Interference (communication); Computer science; Testbed; NeuRFon; Computer network; Wireless; Network packet; Channel (broadcasting); Wireless sensor network; Transmission (telecommunications); Adjacent-channel interference; Wi-Fi; Co-channel interference; Wireless network; Key distribution in wireless sensor networks; Telecommunications","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":[0.0007095524,0.0005713467,0.0003571029,0.0006173477,0.0003968937,0.000506437,0.0006267712,0.000341257,0.0004705816],"category_scores_gemma":[0.003289843,0.0001961987,0.0001872187,0.00030711,0.0003765045,0.0008527873,0.0006782396,0.0005864537,0.0001692754],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000203413,"about_ca_system_score_gemma":0.000483319,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004822095,"about_ca_topic_score_gemma":0.000864848,"domain_scores_codex":[0.9993718,0.0001939014,0.00003542558,0.00008158391,0.0002437456,0.00007355487],"domain_scores_gemma":[0.9983519,0.0006708397,0.0002678095,0.000148578,0.0004851171,0.00007579611],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001273233,0.0004885239,0.008733544,0.0002784988,0.0001093778,0.000391494,0.0005449904,0.06225434,0.5081685,0.003072926,0.001205463,0.4134791],"study_design_scores_gemma":[0.0001082825,0.001566973,0.01292342,0.00004248865,0.0002052806,0.001006525,0.0002343282,0.6637208,0.3153291,0.001806128,0.002947808,0.0001088921],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.219875,0.0006127327,0.7755241,0.0001004489,0.00008754362,0.0001290809,0.00002913984,0.001324527,0.002317443],"genre_scores_gemma":[0.9164149,0.000190734,0.0826465,0.00005159933,0.00002901577,0.00004927053,0.00003561955,0.00002713819,0.000555205],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0007095524,"threshold_uncertainty_score":0.00375247,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03735211635707471,"score_gpt":0.2891857734148234,"score_spread":0.2518336570577487,"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."}}