{"id":"W2060918376","doi":"10.1109/icc.2012.6364358","title":"Received signal strength calibration for handset localization in WLAN","year":2012,"lang":"en","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"University of Victoria","keywords":"RSS; Handset; Calibration; Computer science; Transformation (genetics); Affine transformation; Wireless; Laptop; Signal strength; Key (lock); Real-time computing; SIGNAL (programming language); Non-line-of-sight propagation; Artificial intelligence; Telecommunications; Mathematics; Statistics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0009449405,0.00074266,0.00045511,0.0007808015,0.0003276508,0.0005690525,0.000909186,0.0006247572,0.002511352],"category_scores_gemma":[0.004611148,0.0003665006,0.0003516474,0.0008798392,0.0003862874,0.0007118993,0.0007972615,0.0006788872,0.001370089],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004553298,"about_ca_system_score_gemma":0.0004577141,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001294018,"about_ca_topic_score_gemma":0.001034779,"domain_scores_codex":[0.998462,0.000506005,0.00005116421,0.00021052,0.0006987235,0.00007164091],"domain_scores_gemma":[0.9985734,0.0004456738,0.0002049684,0.000369498,0.0003785506,0.00002784696],"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":[0.0003969402,0.0001976403,0.006461631,0.0002116879,0.0001086924,0.0002697911,0.0002664888,0.1688811,0.1951597,0.004361521,0.001981799,0.621703],"study_design_scores_gemma":[0.00006610317,0.0004771322,0.01051219,0.00005063359,0.00006366023,0.0007439552,0.00009610583,0.7550244,0.2190832,0.002939696,0.01084572,0.00009711192],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03671757,0.0001423704,0.9590496,0.00005597106,0.00004963137,0.00005580317,0.00004159519,0.00189214,0.001995323],"genre_scores_gemma":[0.7357562,0.0002128408,0.2613487,0.00007984888,0.00003294602,0.0001072275,0.0001675021,0.0002182126,0.002076627],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002511352,"threshold_uncertainty_score":0.008401334,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01266039791728463,"score_gpt":0.2204430667540513,"score_spread":0.2077826688367666,"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."}}