{"id":"W2347169061","doi":"10.1017/jog.2016.44","title":"Potential of RADARSAT-2 stereo radargrammetry for the generation of glacier DEMs","year":2016,"lang":"en","type":"article","venue":"Journal of Glaciology","topic":"Cryospheric studies and observations","field":"Earth and Planetary Sciences","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Center for Northern Studies; Université de Sherbrooke","funders":"Centre National d’Etudes Spatiales; Canadian Space Agency; Fonds Québécois de la Recherche sur la Nature et les Technologies; Université de Sherbrooke","keywords":"Glacier; Geology; Remote sensing; Glacier mass balance; Terrain; Ice caps; Geodesy; Physical geography; Geomorphology; Geography; Cartography","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"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":[0.0006736497,0.0004047264,0.0001706374,0.001891416,0.0001304435,0.0005298771,0.0003656829,0.000183012,0.002269214],"category_scores_gemma":[0.001294628,0.0001581458,0.0001950114,0.001551057,0.0001151976,0.0005059996,0.0003904665,0.0002657877,0.001140551],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004960336,"about_ca_system_score_gemma":0.0006775054,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02317217,"about_ca_topic_score_gemma":0.03892621,"domain_scores_codex":[0.9997658,0.00006696649,0.000009675963,0.00003811671,0.00009728218,0.00002215171],"domain_scores_gemma":[0.9992895,0.00009588485,0.00006755708,0.0001835205,0.0003317178,0.00003177054],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0002132371,0.0001236954,0.03943282,0.0002641902,0.0001273512,0.0001507556,0.0001751269,0.1421666,0.03996673,0.003211515,0.0241647,0.7500032],"study_design_scores_gemma":[0.00008670551,0.0001161674,0.1584612,0.0001557064,0.00006581924,0.0001481819,0.0003610842,0.7484699,0.02908047,0.006647376,0.05630472,0.0001027046],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4122268,0.001982439,0.4804148,0.001313279,0.0003040786,0.0004328444,0.05355812,0.01744217,0.03232542],"genre_scores_gemma":[0.7261064,0.0005462572,0.245517,0.0001644305,0.00006964792,0.00009505509,0.02491613,0.0004361495,0.002148937],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.02317217,"threshold_uncertainty_score":0.04607457,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03826993751347758,"score_gpt":0.2441322159077127,"score_spread":0.2058622783942352,"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."}}