{"id":"W4388635584","doi":"10.3390/su152215833","title":"Enhancing Indoor Navigation in Intelligent Transportation Systems with 3D RIF and Quantum GIS","year":2023,"lang":"en","type":"article","venue":"Sustainability","topic":"Traffic Prediction and Management Techniques","field":"Engineering","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Wilfrid Laurier University","funders":"King Saud University","keywords":"Computer science; Intelligent transportation system; Field (mathematics); Routing (electronic design automation); Geographic information system; Transport engineering; Global Positioning System; Systems engineering; Telecommunications; Computer network; Engineering; Remote sensing; Geography","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":[0.0005302476,0.0005182493,0.0004172639,0.0006609333,0.0004202113,0.001224383,0.0007248202,0.0005296506,0.001859445],"category_scores_gemma":[0.001062858,0.0002751017,0.0006439841,0.0009012063,0.0006769926,0.001586945,0.001557288,0.0006099258,0.0006297433],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008577295,"about_ca_system_score_gemma":0.001022023,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01078205,"about_ca_topic_score_gemma":0.01286768,"domain_scores_codex":[0.9995824,0.0001539697,0.00001558736,0.00005832023,0.00015413,0.00003573156],"domain_scores_gemma":[0.9997051,0.0001011944,0.00002895959,0.00005996837,0.00008826093,0.00001642901],"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":[0.00008323101,0.00009192841,0.002747065,0.000184422,0.00005399463,0.0001634362,0.0005960623,0.5874022,0.02012462,0.06106877,0.00389652,0.3235877],"study_design_scores_gemma":[0.00001094321,0.00009944657,0.001085188,0.00003211594,0.00003048446,0.0001275595,0.0002118454,0.951928,0.006163232,0.01784249,0.02241951,0.00004922272],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01768408,0.0002487026,0.9735826,0.0002447273,0.00004420374,0.00003016314,0.0001000439,0.001501486,0.006563915],"genre_scores_gemma":[0.4238694,0.0005106212,0.5730105,0.00009467204,0.00003048173,0.00006336649,0.0002475781,0.0001547462,0.002018713],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01078205,"threshold_uncertainty_score":0.0214386,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005427064994441406,"score_gpt":0.2235066816882747,"score_spread":0.2180796166938333,"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."}}