{"id":"W4387784075","doi":"10.1049/ell2.12988","title":"Received signal strength reconstruction using pix2pix generative adversarial network","year":2023,"lang":"en","type":"article","venue":"Electronics Letters","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"CHIST-ERA; China Scholarship Council","keywords":"RSS; Fingerprint (computing); Computer science; Generative grammar; Artificial intelligence; Signal strength; Generative adversarial network; Machine learning; Wireless; Data mining; Pattern recognition (psychology); Deep learning; Telecommunications; World Wide Web","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":[0.0001180951,0.000162956,0.0001499551,0.0001420117,0.0001623137,0.00003975224,0.000126292,0.0001212694,0.00005305928],"category_scores_gemma":[0.00001618188,0.0001797759,0.00005975271,0.0006514098,0.00005291495,0.0001330228,0.00002280644,0.0002956402,0.00004039366],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002766781,"about_ca_system_score_gemma":0.00003492451,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004803726,"about_ca_topic_score_gemma":0.00001563284,"domain_scores_codex":[0.9989181,0.00003366848,0.0001940628,0.0001856019,0.0001309727,0.0005376537],"domain_scores_gemma":[0.9997273,0.00003751698,0.00003747622,0.000143486,0.00002365289,0.00003056883],"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":[0.00001110622,0.000002311186,0.0001461713,0.000008165416,0.00007572117,0.000006132751,0.0001046736,0.9035479,0.07398067,0.0008528181,0.009586729,0.0116776],"study_design_scores_gemma":[0.0005995135,0.00005163029,0.000085491,0.00002875182,0.00003830175,0.00002033999,0.0001275226,0.864847,0.1242057,0.001530164,0.008007779,0.0004577824],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8432153,0.0002992826,0.1511633,0.0004840442,0.001625785,0.0002143439,0.000008227622,0.002576877,0.0004128442],"genre_scores_gemma":[0.9940812,0.0003201982,0.004241193,0.0002969426,0.0008639625,0.00001825056,0.00007018607,0.0000662132,0.00004184082],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1508659,"threshold_uncertainty_score":0.733105,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009899783210038109,"score_gpt":0.2045854480945399,"score_spread":0.1946856648845018,"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."}}