{"id":"W4323519381","doi":"10.1109/jiot.2023.3253660","title":"Adaptive Path Loss Model for BLE Indoor Positioning System","year":2023,"lang":"en","type":"article","venue":"IEEE Internet of Things Journal","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ontario Tech University","funders":"Coordenação de Aperfeiçoamento de Pessoal de Nível Superior; Samsung Eletrônica da Amazônia","keywords":"Computer science; Global Positioning System; Real-time computing; Path loss; Indoor positioning system; Bluetooth; Hybrid positioning system; Positioning system; Position (finance); GPS signals; SIGNAL (programming language); Simulation; Point (geometry); Assisted GPS; Wireless; Telecommunications; Accelerometer","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":[0.0002830408,0.0001264353,0.0002042019,0.0002472375,0.00006804851,0.00006333978,0.0002750121,0.0001189305,0.000006284771],"category_scores_gemma":[0.00003286463,0.0001177543,0.0001256089,0.0001638142,0.00003476683,0.0002835515,0.00002799293,0.0002572031,0.00002737723],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001351019,"about_ca_system_score_gemma":0.00002032204,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006569093,"about_ca_topic_score_gemma":6.882939e-7,"domain_scores_codex":[0.9991021,0.00001024511,0.0003643815,0.00009724645,0.0001797534,0.0002463149],"domain_scores_gemma":[0.999544,0.00004488604,0.0001143477,0.00009639195,0.0001558648,0.00004456049],"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":[0.0001047706,0.00002064929,0.0002333039,0.0004512982,0.0002926703,0.00006592592,0.006376401,0.950021,0.007794328,0.0085965,0.02291722,0.003125989],"study_design_scores_gemma":[0.0003430657,0.00006533488,0.000009907963,0.0003745199,0.00002013265,0.0001072998,0.0006196649,0.943009,0.0538183,0.001441902,0.00007202318,0.0001188697],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1337414,0.00006454338,0.8636969,0.00004791987,0.000849836,0.0001164281,0.000014926,0.0006688335,0.0007992852],"genre_scores_gemma":[0.9940543,0.00002504344,0.005316618,0.00003096278,0.00008903706,0.00001417847,0.000004054481,0.00003641978,0.0004294235],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8603129,"threshold_uncertainty_score":0.4801882,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01770032503500279,"score_gpt":0.2281204706339995,"score_spread":0.2104201455989967,"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."}}