{"id":"W2194333542","doi":"10.1139/l2012-094","title":"A framework for indoor construction resources tracking by applying wireless sensor networks<sup>1</sup>This paper is one of a selection of papers in this Special Issue on Construction Engineering and Management.","year":2012,"lang":"en","type":"article","venue":"Canadian Journal of Civil Engineering","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Trilateration; Wireless sensor network; Global Positioning System; Reliability (semiconductor); Tracking (education); Computer science; Real-time computing; Wireless; Signal strength; Engineering; Computer network; Telecommunications","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.001095489,0.001212837,0.0008310125,0.001244343,0.001150275,0.002377494,0.002826803,0.001566197,0.003340604],"category_scores_gemma":[0.001000514,0.0006282431,0.00167432,0.001642921,0.001713088,0.002228403,0.003063149,0.001627129,0.001427338],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001077662,"about_ca_system_score_gemma":0.001547201,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006765469,"about_ca_topic_score_gemma":0.008255146,"domain_scores_codex":[0.9991678,0.0002545852,0.00005402755,0.0001777282,0.0002775606,0.00006825216],"domain_scores_gemma":[0.999714,0.00008009052,0.00003747604,0.00005087568,0.00007408307,0.00004350905],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00003695848,0.00006866817,0.0008365574,0.0003792428,0.00007223845,0.0008300933,0.0007112166,0.1162519,0.008565482,0.7698756,0.008098491,0.09427354],"study_design_scores_gemma":[0.00001781568,0.0001119378,0.0006132912,0.0002121949,0.00006954661,0.0009099165,0.0003777102,0.585131,0.00450598,0.1388063,0.2691654,0.00007900461],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0006912204,0.0004933582,0.9933277,0.0001809599,0.000096002,0.00005882981,0.00004930377,0.0003886643,0.004713936],"genre_scores_gemma":[0.04445171,0.002242184,0.9440522,0.00008239745,0.0001392414,0.0003144596,0.0002775679,0.0001447834,0.008295514],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006765469,"threshold_uncertainty_score":0.01345217,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005779558936286598,"score_gpt":0.1819437036131542,"score_spread":0.1761641446768676,"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."}}