{"id":"W2102811245","doi":"10.3390/ijgi4031301","title":"Tracking 3D Moving Objects Based on GPS/IMU Navigation Solution, Laser Scanner Point Cloud and GIS Data","year":2015,"lang":"en","type":"article","venue":"ISPRS International Journal of Geo-Information","topic":"Autonomous Vehicle Technology and Safety","field":"Engineering","cited_by":29,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"","keywords":"Point cloud; Computer vision; Inertial measurement unit; Computer science; Global Positioning System; Artificial intelligence; Mobile mapping; Lidar; Orientation (vector space); Ranging; Video tracking; Object (grammar); 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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008019139,0.0001320111,0.0001454002,0.0003463678,0.00007267183,0.0001151187,0.0004147325,0.0001354956,0.00002003947],"category_scores_gemma":[0.0002567754,0.0001271931,0.00003813682,0.0001024057,0.00004284307,0.003675197,0.0000726557,0.0003472692,0.00003963678],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002802021,"about_ca_system_score_gemma":0.00009478879,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001756761,"about_ca_topic_score_gemma":0.000007739739,"domain_scores_codex":[0.9986923,0.00002565441,0.0005901411,0.00007515796,0.0004663294,0.0001504707],"domain_scores_gemma":[0.998865,0.00005165931,0.0003074883,0.000197521,0.000493199,0.00008514615],"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":[0.0005212674,0.00007631716,0.00237064,0.00008703322,0.0003307559,0.00005537652,0.003955827,0.540647,0.0003648437,0.001475912,0.01379841,0.4363167],"study_design_scores_gemma":[0.001789743,0.0001242056,0.004379112,0.0002522384,0.00003422802,0.0002592278,0.0005963571,0.9759275,0.002149998,0.0008995634,0.01335788,0.0002298996],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.505304,0.0001984714,0.476201,0.005442047,0.005402833,0.0003093582,0.0002130814,0.0003858155,0.006543393],"genre_scores_gemma":[0.9958662,0.00002184978,0.003292562,0.0003249613,0.0002772409,0.000001831656,0.0001976848,0.000009800548,0.000007807696],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4905623,"threshold_uncertainty_score":0.5186782,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01462256813972452,"score_gpt":0.2416774294714729,"score_spread":0.2270548613317484,"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."}}