{"id":"W4388267200","doi":"10.36227/techrxiv.24425536.v1","title":"Fast Selection of Indoor Wireless Transmitter Locations with Generalizable Neural Network Propagation Models","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Leverage (statistics); Transmitter; Fidelity; Computer science; Wireless; Wireless network; Software deployment; Artificial neural network; Radio propagation; Artificial intelligence; Telecommunications","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":[0.0005253006,0.0008954633,0.0005393765,0.000348441,0.0002649614,0.000565997,0.0009857673,0.001069599,0.001376914],"category_scores_gemma":[0.002716509,0.0006267704,0.0003909131,0.000410487,0.0005158964,0.0008642042,0.0006818193,0.001198434,0.0004514709],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007713895,"about_ca_system_score_gemma":0.0007144575,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01057728,"about_ca_topic_score_gemma":0.01300342,"domain_scores_codex":[0.9998179,0.00005534846,0.000006354449,0.00004824867,0.00003944169,0.00003281158],"domain_scores_gemma":[0.999252,0.0004752892,0.00008533069,0.00004543447,0.0001111401,0.00003080495],"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":[0.00002550562,0.00001225069,0.0003366319,0.000008675906,0.000007466122,0.00002051795,0.000008516421,0.987448,0.0005662133,0.0008177128,0.0003306084,0.01041781],"study_design_scores_gemma":[8.754797e-7,0.000001507988,0.00002117944,6.468583e-7,4.687741e-7,0.000001266395,5.741755e-7,0.9996722,0.00007583933,0.0002019743,0.00002298138,5.805206e-7],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0629556,0.0002832233,0.9324549,0.0003020289,0.00005590641,0.00002427293,0.0001008663,0.0008872748,0.002935913],"genre_scores_gemma":[0.8710372,0.0002142019,0.1228676,0.0001464617,0.00005558599,0.00008035963,0.0002802228,0.00009791727,0.005220405],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01057728,"threshold_uncertainty_score":0.02103144,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02066291195788003,"score_gpt":0.2118785364679511,"score_spread":0.1912156245100711,"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."}}