{"id":"W2530306538","doi":"10.1007/s12083-016-0525-5","title":"A noncontact positioning measuring system based on distributed wireless networks","year":2016,"lang":"en","type":"article","venue":"Peer-to-Peer Networking and Applications","topic":"Target Tracking and Data Fusion in Sensor Networks","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"Carleton University","funders":"National Natural Science Foundation of China; Fundamental Research Funds for the Central Universities; National Key Scientific Instrument and Equipment Development Projects of China; National Science Foundation","keywords":"Computer science; Computer vision; Wireless sensor network; Position (finance); Artificial intelligence; Sensor fusion; Wireless; Range (aeronautics); Measure (data warehouse); Coordinate system; Real-time computing; Engineering; Telecommunications; Computer network","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008598945,0.0003185105,0.0003172411,0.0001786853,0.0008579374,0.0004881092,0.0008451701,0.0001404942,0.000004393629],"category_scores_gemma":[0.00003758219,0.0002572539,0.00008706727,0.0009997736,0.0000387928,0.0001676985,0.0002595619,0.0002477273,0.00008830758],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000165577,"about_ca_system_score_gemma":0.00003911242,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003305358,"about_ca_topic_score_gemma":0.00001079643,"domain_scores_codex":[0.9971179,0.0001103189,0.000438025,0.0009475604,0.0007029978,0.0006831513],"domain_scores_gemma":[0.9972589,0.0006104485,0.0001454662,0.001093309,0.0003806402,0.0005112259],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00009592551,0.0002151587,0.004778264,0.0000506486,0.00007136213,0.00002834548,0.0002585524,0.2674338,0.0007213089,0.06324992,0.03173386,0.6313629],"study_design_scores_gemma":[0.000841932,0.0001423955,0.004514974,0.001290276,0.00003864757,0.00003788679,0.00004162685,0.7480938,0.0002019692,0.0002251639,0.2437269,0.0008443917],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001889494,0.00006062941,0.9876365,0.007062809,0.0005598552,0.0005928758,0.0000752724,0.0008095399,0.001313037],"genre_scores_gemma":[0.983874,0.000007974615,0.01317833,0.000826388,0.001300833,0.0004603097,0.00008353253,0.00003784889,0.0002308248],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9819844,"threshold_uncertainty_score":0.999988,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01503991867132043,"score_gpt":0.2335912865171937,"score_spread":0.2185513678458733,"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."}}