{"id":"W2802920593","doi":"10.1016/j.isprsjprs.2018.03.026","title":"Mapping permafrost landscape features using object-based image classification of multi-temporal SAR images","year":2018,"lang":"en","type":"article","venue":"ISPRS Journal of Photogrammetry and Remote Sensing","topic":"Climate change and permafrost","field":"Earth and Planetary Sciences","cited_by":32,"is_retracted":false,"has_abstract":false,"ca_institutions":"Institut National de la Recherche Scientifique; Center for Northern Studies","funders":"Bayerische Forschungsallianz","keywords":"Permafrost; Thermokarst; Remote sensing; Arctic; Land cover; Synthetic aperture radar; Vegetation (pathology); Physical geography; Satellite imagery; Geology; Environmental science; Land use; Geography; Ecology","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":[0.000213163,0.0003034145,0.0002401004,0.00247213,0.0002288939,0.0007553254,0.0002571486,0.0003289632,0.000806941],"category_scores_gemma":[0.0002515513,0.0001610413,0.000392551,0.001383466,0.0001358644,0.0005627018,0.0002106982,0.000165061,0.0002876381],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003218579,"about_ca_system_score_gemma":0.0004302496,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01240025,"about_ca_topic_score_gemma":0.02230653,"domain_scores_codex":[0.9998771,0.000008932354,0.00001007398,0.00003871617,0.00003630207,0.00002880189],"domain_scores_gemma":[0.9998654,0.0000181277,0.00003070345,0.00001463222,0.00005620515,0.00001482413],"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":[0.0004739816,0.000548272,0.1104627,0.0001626918,0.0001897285,0.0003171278,0.0002173512,0.03507966,0.167747,0.000502663,0.001832366,0.6824665],"study_design_scores_gemma":[0.000032151,0.0001037675,0.4094663,0.00002520516,0.0001414354,0.0002619721,0.0003641612,0.5641924,0.02253529,0.0005166967,0.002328086,0.0000325814],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9502329,0.0003292369,0.0447522,0.00009028208,0.00002703466,0.00006784675,0.001074809,0.0004719039,0.002953783],"genre_scores_gemma":[0.9596952,0.000153092,0.03744783,0.00002430539,0.00001668416,0.00002201318,0.0015317,0.0000289225,0.001080199],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01240025,"threshold_uncertainty_score":0.02465612,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04290707876221269,"score_gpt":0.2823867283843765,"score_spread":0.2394796496221638,"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."}}