{"id":"W2766350092","doi":"10.1109/apusncursinrsm.2017.8072508","title":"Characterization of dispersion code multiplexing (DCM) in wireless indoor environment","year":2017,"lang":"en","type":"article","venue":"","topic":"Advanced Wireless Communication Techniques","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Polytechnique Montréal","funders":"","keywords":"Wireless; Multiplexing; Computer science; Chebyshev filter; Electronic engineering; Code (set theory); Dispersion (optics); Statistic; Gaussian; Coding (social sciences); Telecommunications; Physics; Engineering; Optics; Mathematics; Statistics","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":[0.0002048672,0.0004327171,0.0002154579,0.0003351724,0.0003301291,0.0003173045,0.0003166047,0.0003799304,0.0004475579],"category_scores_gemma":[0.0007586643,0.0001119326,0.0001068391,0.0003458066,0.0005519076,0.0003252324,0.0003228314,0.000323774,0.0001855418],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005193026,"about_ca_system_score_gemma":0.0004163774,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001047497,"about_ca_topic_score_gemma":0.0008097563,"domain_scores_codex":[0.9997644,0.00003210779,0.000006399256,0.00003256369,0.0001191026,0.00004543825],"domain_scores_gemma":[0.9993205,0.0002780367,0.0001585439,0.00006468621,0.0001434296,0.00003476592],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0001663056,0.00009980635,0.01005396,0.0001574539,0.00001977573,0.0007696416,0.000344925,0.1662768,0.7581277,0.03998563,0.0005543801,0.02344352],"study_design_scores_gemma":[0.00001222328,0.0002963819,0.006620625,0.00002184035,0.00001078769,0.001064865,0.0001707215,0.6835118,0.2993097,0.006344923,0.002571695,0.00006455295],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6689053,0.0002222387,0.3221413,0.0001911999,0.00002391774,0.00005824288,0.0002148606,0.0003026162,0.007940356],"genre_scores_gemma":[0.9881598,0.00009903841,0.01101532,0.00002877063,0.000010147,0.00002366797,0.00005135508,0.00001391915,0.0005981057],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001047497,"threshold_uncertainty_score":0.003767848,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01670958259660695,"score_gpt":0.2431968526260061,"score_spread":0.2264872700293991,"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."}}