{"id":"W2893778226","doi":"10.5194/isprs-archives-xlii-1-457-2018","title":"MOBILE LASER SCANNING SYSTEMS FOR GPS/GNSS-DENIED ENVIRONMENT MAPPING","year":2018,"lang":"en","type":"article","venue":"The international archives of the photogrammetry, remote sensing and spatial information sciences/International archives of the photogrammetry, remote sensing and spatial information sciences","topic":"Robotics and Sensor-Based Localization","field":"Engineering","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"National Natural Science Foundation of China; National Science Foundation","keywords":"Point cloud; Lidar; Mobile mapping; GNSS applications; Computer science; Global Positioning System; Remote sensing; Calibration; Laser scanning; Simultaneous localization and mapping; Computer vision; Artificial intelligence; Feature (linguistics); Geography; Laser; Optics; Mobile robot; Telecommunications; Mathematics","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.001135532,0.00039721,0.0003968693,0.0007843644,0.001274976,0.0007589564,0.00115758,0.00009771447,0.000006477695],"category_scores_gemma":[0.0003138851,0.0002678125,0.0003034032,0.0006367958,0.002267722,0.00040138,0.000468267,0.0002917013,0.000004353525],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006665782,"about_ca_system_score_gemma":0.0001143121,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.4140061,"about_ca_topic_score_gemma":0.0329237,"domain_scores_codex":[0.9962643,0.0001631586,0.001345083,0.0003434154,0.001368695,0.0005152851],"domain_scores_gemma":[0.9975147,0.0006814372,0.0009781617,0.000422203,0.0002593441,0.000144177],"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":[0.00007731518,0.00001148394,0.00008281957,0.00006401778,0.00009196531,1.389489e-7,0.003130985,0.1044488,0.002834271,0.000008135401,0.00009760918,0.8891524],"study_design_scores_gemma":[0.0006857145,0.0001710257,0.0006090561,0.00034232,0.00003911433,0.00005823709,0.002179888,0.9718524,0.009746389,0.001750028,0.01225638,0.0003094522],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01049939,0.00005724403,0.9791865,0.0007367707,0.002452275,0.0009289152,0.0001118358,0.00007033977,0.005956729],"genre_scores_gemma":[0.9945819,0.0002413099,0.004277621,0.0004526529,0.0002640083,0.000001154592,0.00005367795,0.00001829862,0.0001093788],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9840825,"threshold_uncertainty_score":0.9999774,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01644292873120431,"score_gpt":0.2358112941815854,"score_spread":0.219368365450381,"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."}}