{"id":"W2516986061","doi":"10.1177/1550147716660904","title":"Correlation-sum-deviation ranging method for vehicular node based on IEEE 802.11p short preamble","year":2016,"lang":"en","type":"article","venue":"International Journal of Distributed Sensor Networks","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Computer science; Ranging; Real-time computing; Multipath propagation; Satellite navigation; BeiDou Navigation Satellite System; Global Positioning System; Satellite system; Channel (broadcasting); Telecommunications; GNSS applications","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":[0.0003807748,0.0001835654,0.0002371534,0.00024551,0.00006263854,0.00006124666,0.0003143703,0.0002011966,0.0000360846],"category_scores_gemma":[0.00028291,0.0001408,0.0002134236,0.0001645121,0.00002605674,0.0002000776,0.00001193379,0.0002073385,0.000005778004],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003121942,"about_ca_system_score_gemma":0.00002824125,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000001323524,"about_ca_topic_score_gemma":0.000001278353,"domain_scores_codex":[0.9985933,0.0000443465,0.0005674697,0.0001405049,0.0004177672,0.00023664],"domain_scores_gemma":[0.9983827,0.0005429153,0.0001851742,0.0001470728,0.0006793385,0.00006281511],"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":[0.0001338028,0.0000287895,0.001480997,0.000007178901,0.0001490645,0.00001411846,0.00001179162,0.9785557,0.001221737,0.0003520161,0.004186213,0.0138586],"study_design_scores_gemma":[0.001224608,0.00006424259,0.000799312,0.0002268416,0.00005066024,0.00002468058,0.00002344269,0.9800196,0.01174193,0.0002072633,0.005435886,0.0001815469],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005232939,0.00007243189,0.9911447,0.0007331341,0.002192791,0.0001676231,0.0001591391,0.0001678763,0.000129338],"genre_scores_gemma":[0.9884084,0.00005860745,0.0106023,0.000144889,0.0005816204,0.00001213579,0.0001317585,0.00003679483,0.00002345758],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9831755,"threshold_uncertainty_score":0.5741656,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.00968330902095127,"score_gpt":0.2530867574653377,"score_spread":0.2434034484443864,"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."}}