{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001477232,0.0002281077,0.0001872683,0.0005148155,0.0001914025,0.0007697608,0.0003665714,0.0003075278,0.001177031],"category_scores_gemma":[0.0003565185,0.0002823085,0.000317438,0.0005709932,0.000205437,0.0004614279,0.0004320811,0.0002662354,0.0004458961],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002438554,"about_ca_system_score_gemma":0.0004046355,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005941075,"about_ca_topic_score_gemma":0.008716088,"domain_scores_codex":[0.9998299,0.00002145072,0.000007694536,0.00003373069,0.0000925142,0.00001467536],"domain_scores_gemma":[0.9998765,0.00003347448,0.0000181639,0.00002739549,0.00003619185,0.000008216358],"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":[0.0002645872,0.000141184,0.03772281,0.0002111686,0.00008669513,0.0003741917,0.000590426,0.604304,0.1229441,0.002234183,0.0007987443,0.230328],"study_design_scores_gemma":[0.00001178108,0.00004382257,0.01251677,0.000008480999,0.00001030027,0.00009630668,0.00009294213,0.9741183,0.01060532,0.000673286,0.001807686,0.00001493648],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4565799,0.0001177034,0.5368291,0.00006001836,0.00001342973,0.00008316338,0.0005335446,0.001626592,0.004156487],"genre_scores_gemma":[0.8096528,0.0001065505,0.1876492,0.00001609443,0.00000505646,0.00005792764,0.0006390398,0.00008991925,0.001783573],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.005941075,"threshold_uncertainty_score":0.01181298,"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."}}