{"id":"W7117560966","doi":"10.1109/mswim67937.2025.11309178","title":"Low-Error Indoor Positioning via Synthetic RSSI Augmentation and Zx–WKNN Hybrid Model","year":2025,"lang":"","type":"article","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Custom Security Industries (Canada); Sheridan College","funders":"Research and Development; Natural Sciences and Engineering Research Council of Canada","keywords":"Mean squared error; Scalability; Multipath interference; Received signal strength indication; Feature (linguistics); Multipath propagation; Key (lock); Pattern recognition (psychology); Interference (communication); Point (geometry)","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":[0.0005243206,0.0006396954,0.000477433,0.000276268,0.0001816596,0.0004923682,0.001008743,0.00061667,0.0006243822],"category_scores_gemma":[0.001368833,0.0004004614,0.0005332939,0.0003476443,0.0004110313,0.0006753895,0.000624517,0.0007223568,0.0003762695],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004407506,"about_ca_system_score_gemma":0.0004152588,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009905543,"about_ca_topic_score_gemma":0.008000288,"domain_scores_codex":[0.9997477,0.0000601295,0.00001276834,0.0000870122,0.00006435579,0.00002804493],"domain_scores_gemma":[0.999587,0.000174788,0.00004899851,0.00007054662,0.0001047713,0.00001397686],"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":[0.00005257029,0.00001932227,0.0007312596,0.000018443,0.00001955969,0.00002924309,0.00003275646,0.9608378,0.003184579,0.0007918796,0.0002718473,0.03401076],"study_design_scores_gemma":[0.000001146786,0.000006581632,0.0001314808,9.811873e-7,0.000002137454,0.000005314482,0.000001870119,0.99914,0.0004852519,0.0001406831,0.00008228424,0.000002214956],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.07568565,0.0001654679,0.9207595,0.0001486765,0.00004885408,0.00001864477,0.0000932605,0.001194523,0.001885365],"genre_scores_gemma":[0.8879152,0.0001480994,0.1082654,0.00008427738,0.00002648897,0.00005048603,0.0003289123,0.0000792961,0.00310191],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.009905543,"threshold_uncertainty_score":0.01969576,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.006657282563554543,"score_gpt":0.2302611919808406,"score_spread":0.2236039094172861,"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."}}