{"id":"W4396542406","doi":"10.1109/jsen.2024.3393851","title":"Object Reconstruction and Localization in Indoor Environments Using Infrastructure Sensor Node","year":2024,"lang":"en","type":"article","venue":"IEEE Sensors Journal","topic":"Robotics and Automated Systems","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta; University of Waterloo","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Wireless sensor network; Computer science; Object (grammar); Node (physics); Computer vision; Sensor node; Object detection; Artificial intelligence; Key distribution in wireless sensor networks; Real-time computing; Computer network; Wireless; Pattern recognition (psychology); Telecommunications; Physics; Acoustics; Wireless network","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":[0.000125413,0.000124372,0.0001368115,0.0002307473,0.00006011102,0.0001580274,0.0000313838,0.0001094786,0.00002248527],"category_scores_gemma":[0.000006722822,0.0001154771,0.00002973955,0.0001528741,0.00002308748,0.0001837734,0.000004812876,0.0003116495,0.0000084285],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001563708,"about_ca_system_score_gemma":0.00001337493,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000006530775,"about_ca_topic_score_gemma":0.000001548832,"domain_scores_codex":[0.999261,0.00004214963,0.0002830623,0.0001129854,0.0001265805,0.0001742295],"domain_scores_gemma":[0.9998232,0.00001635697,0.00003219729,0.00005855773,0.000007550071,0.0000621494],"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":[0.000002483472,0.000001780211,0.003454535,0.00004965319,0.00002866956,0.00008477053,0.0002927839,0.9482914,0.04561451,0.000002051644,0.0001250734,0.002052315],"study_design_scores_gemma":[0.0001827367,0.000009697832,0.001910687,0.0002904197,0.00001420243,0.003056691,0.0001375427,0.9888605,0.004697026,0.00008162217,0.0006259165,0.0001329429],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.976435,0.0005637105,0.02065864,0.00001017947,0.002086304,0.00006650586,0.000004283007,0.00005678741,0.0001186146],"genre_scores_gemma":[0.998174,0.00033495,0.001154493,0.000009414698,0.0002726317,4.315334e-7,0.000001033494,0.00003293319,0.00002011782],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04091749,"threshold_uncertainty_score":0.470902,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.008384894923433242,"score_gpt":0.213338210878078,"score_spread":0.2049533159546447,"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."}}