{"id":"W4245211266","doi":"10.22215/etd/2011-07196","title":"Real-time localization in large-scale underground environments using RFID-based node maps","year":2011,"lang":"en","type":"dissertation","venue":"","topic":"Indoor and Outdoor Localization Technologies","field":"Engineering","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Node (physics); Scale (ratio); Computer graphics (images); Cartography; Humanities; Geography; Engineering; Art","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":[0.0001558555,0.0002903513,0.0001440839,0.0003009088,0.0001324255,0.0004502372,0.0003299706,0.0002762525,0.001672807],"category_scores_gemma":[0.00062414,0.0001488322,0.0001349408,0.0003926973,0.0001656724,0.000720792,0.0005002727,0.000191128,0.0008958696],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000140614,"about_ca_system_score_gemma":0.000160649,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001342238,"about_ca_topic_score_gemma":0.00230097,"domain_scores_codex":[0.9998366,0.00003217917,0.000005113762,0.00004057937,0.0000638724,0.00002166238],"domain_scores_gemma":[0.9997788,0.0000903914,0.00002825031,0.0000349992,0.00005413333,0.00001344817],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006378366,0.0001408669,0.005975597,0.0002387541,0.00006067152,0.0005593479,0.0006821304,0.2103862,0.225853,0.008026603,0.00672206,0.5407169],"study_design_scores_gemma":[0.00007206527,0.0005460329,0.0121972,0.00005394023,0.00007108779,0.0007111675,0.0007842769,0.8200358,0.1271422,0.00542791,0.03288199,0.00007630594],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2051536,0.0005543082,0.7797221,0.0002326555,0.0001195419,0.00003471888,0.0002224615,0.002284593,0.01167602],"genre_scores_gemma":[0.8796397,0.00052091,0.1017468,0.00001772964,0.00003086645,0.00002999143,0.0003031842,0.00008444748,0.01762648],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.001672807,"threshold_uncertainty_score":0.005596101,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01005679521855221,"score_gpt":0.2198786596882449,"score_spread":0.2098218644696927,"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."}}