{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008032928,0.000677848,0.0005851491,0.0003255637,0.0002720921,0.0006503158,0.001031299,0.0007217237,0.002063828],"category_scores_gemma":[0.0015841,0.0003735253,0.0005530445,0.0003144065,0.0009765234,0.0007094378,0.001401158,0.001084074,0.0003321198],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007136719,"about_ca_system_score_gemma":0.0003802599,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002252507,"about_ca_topic_score_gemma":0.001491505,"domain_scores_codex":[0.9996217,0.0001212989,0.00001048889,0.00009193431,0.0001106933,0.00004376166],"domain_scores_gemma":[0.999241,0.0004528641,0.00009660058,0.00008799702,0.00008527176,0.00003626521],"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":[0.00005549445,0.00001053153,0.0004030754,0.00001388623,0.0000168251,0.00006996049,0.00002424965,0.9801527,0.001120025,0.008500267,0.0003899958,0.009243057],"study_design_scores_gemma":[0.000001731735,0.00001021347,0.00003570959,0.000001478197,0.000002713501,0.00001524784,0.000002253698,0.9984517,0.000367963,0.0009784327,0.0001301975,0.00000229677],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01726306,0.0001146308,0.9794932,0.0001891028,0.0000280539,0.00002731907,0.00003762578,0.0002788446,0.002568167],"genre_scores_gemma":[0.9391794,0.0002028465,0.05414442,0.0001405946,0.00002482168,0.00007293856,0.0001042062,0.00005099551,0.006079871],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002252507,"threshold_uncertainty_score":0.006904244,"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."}}