{"id":"W2511144089","doi":"10.3390/rs8090697","title":"Land Cover Classification in SubArctic Regions Using Fully Polarimetric RADARSAT-2 Data","year":2016,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Climate change and permafrost","field":"Earth and Planetary Sciences","cited_by":20,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval; Center for Northern Studies; Université du Québec à Trois-Rivières; Institut National de la Recherche Scientifique","funders":"Canadian Forest Service; Canadian Natural Resources Limited; Canadian Space Agency; Natural Resources Canada; Natural Sciences and Engineering Research Council of Canada; ArcticNet","keywords":"Remote sensing; Subarctic climate; Polarimetry; Environmental science; Land cover; Geology; Land use; Oceanography","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":[0.0005357113,0.0004271281,0.0002675195,0.001345227,0.0001428121,0.000390212,0.0001507658,0.0002010389,0.0003830005],"category_scores_gemma":[0.0004949034,0.0001095059,0.0002606408,0.0008150195,0.0001167306,0.0002940566,0.0001928688,0.000148031,0.0002525111],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003055772,"about_ca_system_score_gemma":0.0002357826,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.020579,"about_ca_topic_score_gemma":0.03044604,"domain_scores_codex":[0.9998424,0.0000420393,0.00001171388,0.00003499773,0.00003692363,0.00003184832],"domain_scores_gemma":[0.9997018,0.0000585489,0.00006142911,0.00004295388,0.0001013471,0.00003402991],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001528168,0.0006126945,0.6046906,0.0001911556,0.0004165013,0.0006756334,0.0002473787,0.1037488,0.06778502,0.000266539,0.00304101,0.2167965],"study_design_scores_gemma":[0.00002575077,0.0001047848,0.8415209,0.00001700544,0.00007415227,0.00009222161,0.0002320969,0.1493583,0.007711757,0.00008514542,0.0007609114,0.00001686219],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9966418,0.0001298272,0.001600552,0.00001923613,0.000007333047,0.00001232381,0.001006318,0.00007715357,0.0005054032],"genre_scores_gemma":[0.9878684,0.0001668213,0.006087461,0.00001571698,0.00001230106,0.00001392426,0.00550539,0.00001230688,0.0003177609],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.020579,"threshold_uncertainty_score":0.04091841,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1648995324803071,"score_gpt":0.2958314729622303,"score_spread":0.1309319404819232,"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."}}