{"id":"W2061795786","doi":"10.1109/wcnc.2014.6951921","title":"Quantitative comparison of indoor RFID channel models using bootstrap techniques","year":2014,"lang":"en","type":"article","venue":"","topic":"Microwave and Dielectric Measurement Techniques","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Aalborg Universitet; University of Calgary","keywords":"Resampling; Computer science; Goodness of fit; Consistency (knowledge bases); Reliability (semiconductor); Path loss; Channel (broadcasting); Wireless; Statistical hypothesis testing; Measure (data warehouse); Data mining; Statistics; Algorithm; Artificial intelligence; Mathematics; Machine learning","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0002469519,0.0001425478,0.0002801627,0.0001604598,0.00002761784,0.00001206702,0.0001242642,0.00009008915,0.00002141354],"category_scores_gemma":[0.00001680676,0.0001334365,0.00005433677,0.0001689233,0.00002599728,0.0001350538,0.00001641235,0.0001110264,0.000002015069],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003107975,"about_ca_system_score_gemma":0.000009631191,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00005914357,"about_ca_topic_score_gemma":0.00001168895,"domain_scores_codex":[0.9991893,0.00002694435,0.000311721,0.0001220537,0.0001550681,0.0001949331],"domain_scores_gemma":[0.9996181,0.00003495799,0.00005370285,0.0001687422,0.00008419952,0.00004026547],"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":[0.00001449791,0.00006169279,0.0001824675,0.0001379354,0.00004932042,2.651811e-7,0.000405121,0.004325261,0.9661167,0.02043472,0.003020713,0.005251324],"study_design_scores_gemma":[0.00004867625,0.00008703078,0.000006317098,0.00003081977,0.000008671323,5.621876e-7,0.00003872831,0.4016039,0.5947508,0.003209057,0.0001138872,0.0001015646],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07251329,0.0002983993,0.8907331,0.000005903977,0.00002999391,0.0001921933,0.000002125936,0.0006571767,0.03556779],"genre_scores_gemma":[0.9477471,0.00003249731,0.05212209,0.00002351815,0.00002056651,0.00001034303,0.000002888487,0.00002317946,0.0000178194],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8752338,"threshold_uncertainty_score":0.5441384,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1308359357023616,"score_gpt":0.3235418208597882,"score_spread":0.1927058851574265,"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."}}