{"id":"W3210281694","doi":"10.1007/978-3-030-89880-9_7","title":"Enhancing Micro-location Accuracy for Asset Tracking: An Evaluation of Two Fingerprinting Approaches Using Three Machine Learning Algorithms","year":2021,"lang":"en","type":"book-chapter","venue":"Lecture notes in networks and systems","topic":"Digital Media Forensic Detection","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"Sheridan College","funders":"","keywords":"Beacon; Computer science; Algorithm; Granularity; Global Positioning System; Real-time computing; Artificial intelligence; Bottleneck; Machine learning; Embedded system; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.002573568,0.0003477462,0.0006025448,0.0002310291,0.0001380093,0.0004580696,0.0002632873,0.0003701154,0.000001253792],"category_scores_gemma":[0.000664749,0.0003444406,0.00009556796,0.0001899224,0.00004446613,0.0004818594,0.0001259896,0.0005317909,1.650772e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001789206,"about_ca_system_score_gemma":0.0001488621,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001416841,"about_ca_topic_score_gemma":0.000763576,"domain_scores_codex":[0.9975859,0.0001516363,0.0007405935,0.0007198285,0.0005033544,0.0002986676],"domain_scores_gemma":[0.9973611,0.0008454815,0.0008321438,0.0004101089,0.0004908698,0.0000602553],"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":[0.00000470819,0.000009174316,0.0001180085,0.0001827637,0.00003565744,0.000001478259,0.0002981102,0.5664871,0.0003651658,0.001495207,2.152266e-7,0.4310023],"study_design_scores_gemma":[0.0003784292,0.00008249591,0.00003639341,0.001569286,0.00007744057,0.00005214636,0.00001274965,0.9924415,0.0009070765,0.004055925,0.00006882,0.0003177653],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005002879,0.007395233,0.9848439,0.00001387424,0.001374832,0.0009476676,0.000003465398,0.00005467981,0.0003634628],"genre_scores_gemma":[0.9754066,0.0000274952,0.02346146,0.0000112935,0.0008158419,0.00004445462,0.0001189262,0.00005636543,0.00005751606],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.9704038,"threshold_uncertainty_score":0.9999008,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09236233541728936,"score_gpt":0.2953787503540439,"score_spread":0.2030164149367545,"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."}}