{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005673644,0.0005622997,0.0004751755,0.001218177,0.0002847893,0.0006030426,0.0006758232,0.0004736522,0.0005559471],"category_scores_gemma":[0.001376031,0.0002407422,0.0003658876,0.0008336882,0.0002470071,0.0005928554,0.0009801782,0.0002798874,0.0003383048],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003842037,"about_ca_system_score_gemma":0.000615512,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003544974,"about_ca_topic_score_gemma":0.004646098,"domain_scores_codex":[0.9989805,0.00009436208,0.0000417736,0.0001421192,0.0006620616,0.00007918517],"domain_scores_gemma":[0.999227,0.0001197155,0.0001718853,0.0001316721,0.0003204299,0.00002921475],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004071185,0.0001426443,0.05102823,0.0002761612,0.0001528597,0.0003805286,0.0003187143,0.07798858,0.406031,0.001217241,0.001964734,0.4600922],"study_design_scores_gemma":[0.00003308609,0.0004083388,0.06148356,0.00003589869,0.00006792173,0.0003801827,0.0002414374,0.7866972,0.1459124,0.0009347192,0.003737796,0.00006746505],"study_design_candidate":"bench_or_experimental","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4957019,0.0002847707,0.4988994,0.0001090131,0.00004871648,0.0001086049,0.0003055645,0.00273651,0.001805462],"genre_scores_gemma":[0.8288792,0.00009292465,0.170029,0.00003668467,0.00001557282,0.00006738212,0.0002638647,0.00005070026,0.0005645921],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.003544974,"threshold_uncertainty_score":0.007048726,"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."}}