{"id":"W3123042981","doi":"10.3390/app11031007","title":"Automated Accuracy Assessment of a Mobile Mapping System with Lightweight Laser Scanning and MEMS Sensors","year":2021,"lang":"en","type":"article","venue":"Applied Sciences","topic":"3D Surveying and Cultural Heritage","field":"Earth and Planetary Sciences","cited_by":17,"is_retracted":false,"has_abstract":true,"ca_institutions":"NovAtel (Canada); University of Calgary","funders":"","keywords":"Mobile mapping; Point cloud; Laser scanning; Scanner; Computer science; Total station; Cloud computing; Microelectromechanical systems; Remote sensing; Real-time computing; Computer vision; Artificial intelligence; Laser; Geography; Cartography; Materials science","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":[0.0005727258,0.0001199714,0.0002151376,0.00004970274,0.0003822992,0.0001377327,0.0001441928,0.00003776391,0.0001258482],"category_scores_gemma":[0.00001057612,0.00007529341,0.00002067252,0.000644714,0.0002371857,0.0001773507,0.0000177878,0.00007671135,0.00001064713],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000004759854,"about_ca_system_score_gemma":0.0001191816,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002725388,"about_ca_topic_score_gemma":0.000284592,"domain_scores_codex":[0.998776,0.00005901914,0.0002034107,0.0003392477,0.0003684205,0.0002539697],"domain_scores_gemma":[0.9994202,0.0002064831,0.0001173066,0.0001151834,0.00005145483,0.00008939629],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002908134,0.00005916117,0.9053813,0.0005207781,0.0001120219,0.0002016363,0.004460628,0.04537546,0.01728321,0.001603912,0.0003274216,0.02464536],"study_design_scores_gemma":[0.0007070078,0.0003044858,0.7527632,0.0004590519,0.00003605564,0.0002471702,0.04887689,0.1806496,0.01402768,0.00005166865,0.001202204,0.000675046],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9699167,0.0002748047,0.00005011724,0.00003173897,0.00007532944,0.0001294692,0.00001530235,0.0001393387,0.02936719],"genre_scores_gemma":[0.9959899,0.00001452554,0.003827359,0.00002910119,0.0000213517,0.000004371261,0.00001651567,0.000002097418,0.00009481328],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1526182,"threshold_uncertainty_score":0.3070377,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01930391622410862,"score_gpt":0.244863982247364,"score_spread":0.2255600660232554,"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."}}