{"id":"W2157851304","doi":"10.1016/j.isatra.2013.09.016","title":"Mobile robot trajectory tracking using noisy RSS measurements: An RFID approach","year":2013,"lang":"en","type":"article","venue":"ISA Transactions","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":30,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Ottawa","funders":"","keywords":"RSS; Beacon; Mobile robot; Robot; Controller (irrigation); Trajectory; Computer science; Tracking (education); Real-time computing; Linearization; Simulation; Control theory (sociology); Artificial intelligence; Control (management)","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004515034,0.0009228433,0.001059413,0.0009163817,0.0003610833,0.001020901,0.001040335,0.001201462,0.000685121],"category_scores_gemma":[0.001967634,0.0007039208,0.0007183974,0.00152163,0.0005822961,0.001621216,0.0008610701,0.0007016661,0.0008784952],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003154345,"about_ca_system_score_gemma":0.0003406061,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001481336,"about_ca_topic_score_gemma":0.001777653,"domain_scores_codex":[0.9993916,0.0001420133,0.0000317786,0.0001862368,0.0001921233,0.00005621067],"domain_scores_gemma":[0.999256,0.0002373442,0.0001396314,0.0001692635,0.0001760841,0.00002171574],"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":[0.0005350405,0.0001560439,0.005717542,0.0003657576,0.0003245211,0.0006404852,0.0002953014,0.465549,0.07794043,0.01281611,0.001872919,0.4337869],"study_design_scores_gemma":[0.00001627983,0.0001262961,0.001922501,0.00002334906,0.0001107462,0.0004187438,0.000059603,0.976857,0.01389742,0.004407561,0.002120786,0.00003967524],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01240858,0.0004363148,0.9856236,0.00006848048,0.0000650616,0.000006689684,0.00003314574,0.0002960325,0.001062118],"genre_scores_gemma":[0.6932897,0.001448711,0.298649,0.0001200928,0.0002691736,0.00003995007,0.0002037709,0.0001165608,0.005863035],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001481336,"threshold_uncertainty_score":0.002945483,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05007859431577538,"score_gpt":0.2470894743860935,"score_spread":0.1970108800703182,"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."}}