{"id":"W4413982218","doi":"10.36680/j.itcon.2025.056","title":"A Deployable Solution for Indoor Tracking of Workers in Construction Sites through Bluetooth Low Energy Technology","year":2025,"lang":"en","type":"article","venue":"Journal of Information Technology in Construction","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Bluetooth Low Energy; Bluetooth; Architectural engineering; Tracking (education); Engineering; Low energy; Energy (signal processing); Systems engineering; Automotive engineering; Embedded system; Computer science; Civil engineering; Telecommunications; Wireless; Physics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002899504,0.0001832471,0.0004353741,0.004744755,0.0000697123,0.00002783771,0.0002692443,0.000799443,0.000006235124],"category_scores_gemma":[0.0004832314,0.0001938348,0.00008436714,0.002736731,0.0004077638,0.00137233,0.00003907711,0.0004778078,9.334829e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003822139,"about_ca_system_score_gemma":0.0001446202,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002089764,"about_ca_topic_score_gemma":0.00006191358,"domain_scores_codex":[0.9980468,0.00001778631,0.001412829,0.0001026788,0.0001427163,0.0002771606],"domain_scores_gemma":[0.9986778,0.00007307754,0.0006280341,0.0001852916,0.0004221693,0.00001360166],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002265146,0.00006501058,0.1675297,0.0004567612,0.0001522161,0.00000373768,0.000352964,0.02885982,0.01119472,0.1956513,0.000489897,0.5950173],"study_design_scores_gemma":[0.004282419,0.0002518711,0.001588108,0.001077601,0.00005982559,0.0003959545,0.01366715,0.03669327,0.7309688,0.2087759,0.001856586,0.0003824549],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3324738,0.000328896,0.6648583,0.0006890859,0.0007604677,0.0002410625,0.000007118927,0.0003233622,0.0003178053],"genre_scores_gemma":[0.9495409,0.0003670004,0.04999728,0.00002637589,0.00001618943,0.00003010329,0.000008504031,0.00001073106,0.000002980188],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7197741,"threshold_uncertainty_score":0.7904354,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005253542803384822,"score_gpt":0.2200820198336596,"score_spread":0.2148284770302748,"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."}}