{"id":"W4400425654","doi":"10.1049/ell2.13265","title":"Efficient high‐fidelity deep convolutional generative adversarial network model for received signal strength reconstruction in indoor environments","year":2024,"lang":"en","type":"article","venue":"Electronics Letters","topic":"Millimeter-Wave Propagation and Modeling","field":"Engineering","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"RSS; Software deployment; Computer science; Deep learning; Wireless; Wireless network; Generative grammar; Interpolation (computer graphics); Fidelity; Artificial intelligence; Machine learning; Generative model; Radio propagation; Computer engineering; Distributed computing; Real-time computing; Telecommunications; Motion (physics); Software engineering","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":[0.0002677476,0.0001809536,0.000158378,0.0001048449,0.00008017895,0.00003936953,0.00007480258,0.00009221784,0.00003887603],"category_scores_gemma":[0.00000836774,0.0002009318,0.00007922281,0.0001194335,0.00002983892,0.00007379227,0.00001481087,0.0003251146,0.000009180186],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000754936,"about_ca_system_score_gemma":0.00006158611,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004497342,"about_ca_topic_score_gemma":0.00003208697,"domain_scores_codex":[0.998719,0.0000427093,0.0003241174,0.000308901,0.0001610169,0.0004442266],"domain_scores_gemma":[0.9997463,0.00006259413,0.00003039752,0.00009616977,0.00001247671,0.00005212299],"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":[0.00004639235,0.00001036054,0.000009082905,0.00001626566,0.00006159396,9.326916e-7,0.0001550536,0.9175417,0.07623723,0.0003443368,0.000300004,0.005277047],"study_design_scores_gemma":[0.0005801197,0.00003314177,0.00001688801,0.00002696057,0.00002384572,0.000003228254,0.000008569531,0.9903029,0.007534386,0.001044677,0.000220433,0.0002048633],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.363322,0.0006246865,0.6351339,0.0001540427,0.0004180553,0.0002439846,0.00001897001,0.00006958138,0.00001481956],"genre_scores_gemma":[0.9829943,0.00008407977,0.01609195,0.0002261219,0.000342692,0.000093811,0.0001172219,0.00003530801,0.00001458281],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6196722,"threshold_uncertainty_score":0.8193762,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.009269316335828089,"score_gpt":0.2001803141307474,"score_spread":0.1909109977949194,"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."}}