{"id":"W3047416124","doi":"10.5194/isprs-annals-v-2-2020-749-2020","title":"MODELLING PERMAFROST TERRAIN USING KINEMATIC, DUAL-WAVELENGTH LASER SCANNING","year":2020,"lang":"en","type":"article","venue":"ISPRS annals of the photogrammetry, remote sensing and spatial information sciences","topic":"Remote Sensing and LiDAR Applications","field":"Environmental Science","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"Strategic Research Council; Natural Resources Canada; Academy of Finland","keywords":"Point cloud; Remote sensing; Terrain; GNSS applications; Permafrost; Standard deviation; Geodesy; Geology; Kinematics; Lidar; Laser scanning; Residual; Computer science; Global Positioning System; Laser; Computer vision; Geography; Telecommunications; Optics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0006967211,0.0001822314,0.0002293498,0.00008345608,0.0006992997,0.0002190419,0.0002221112,0.00006901215,0.00002270548],"category_scores_gemma":[0.0001949067,0.0001339986,0.0001098142,0.0008693906,0.0006380971,0.0003520081,0.0002132631,0.0001686215,0.00001603546],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001783713,"about_ca_system_score_gemma":0.00003251416,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1154846,"about_ca_topic_score_gemma":0.001120984,"domain_scores_codex":[0.9982206,0.00009080814,0.0005220996,0.0002382872,0.000599622,0.0003286452],"domain_scores_gemma":[0.9990658,0.00007696858,0.000400038,0.0002325853,0.00006225277,0.0001624143],"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":[0.00001368535,0.000007911806,0.0001753988,0.00002612965,0.00001029799,4.041856e-7,0.004910423,0.2981482,0.004057277,0.000002325615,0.0004015479,0.6922464],"study_design_scores_gemma":[0.0001113677,0.00004271392,0.0003157801,0.00006505375,0.00001377786,0.00001922842,0.001119012,0.9854175,0.01029386,0.0001978845,0.002235135,0.0001686304],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4188695,0.00001180587,0.5745417,0.003418279,0.0001026626,0.0002427442,0.00001745747,0.00003845696,0.002757352],"genre_scores_gemma":[0.9861472,0.00002031574,0.01187103,0.00188345,0.0000503823,5.602642e-8,0.000005167208,0.000008250599,0.00001417516],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6920778,"threshold_uncertainty_score":0.8904055,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06601523155685321,"score_gpt":0.2911595171514673,"score_spread":0.2251442855946141,"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."}}