{"id":"W1592469096","doi":"10.3390/rs70709410","title":"Potential of C and X Band SAR for Shrub Growth Monitoring in Sub-Arctic Environments","year":2015,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Climate change and permafrost","field":"Earth and Planetary Sciences","cited_by":51,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ministère des Ressources naturelles et des Forêts; Université Laval; Université du Québec à Trois-Rivières; Institut National de la Recherche Scientifique","funders":"Natural Sciences and Engineering Research Council of Canada; Canadian Space Agency; Centre National de la Recherche Scientifique; ArcticNet","keywords":"Shrub; Tundra; Arctic; Vegetation (pathology); Arctic vegetation; Remote sensing; The arctic; Environmental science; Physical geography; Geology; Geography; Ecology; Oceanography; Biology","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":[0.0005050046,0.000447133,0.0002067016,0.0007215329,0.0001413191,0.0003437099,0.0002480143,0.0001779856,0.0005211147],"category_scores_gemma":[0.0003422882,0.0001203908,0.0001452591,0.0007072328,0.0001205057,0.0002868956,0.0001432051,0.0001163246,0.0001943258],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002770744,"about_ca_system_score_gemma":0.0004294571,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.04546307,"about_ca_topic_score_gemma":0.08466294,"domain_scores_codex":[0.9998498,0.00004738352,0.000004708265,0.0000340294,0.00003938406,0.00002469935],"domain_scores_gemma":[0.9996482,0.0000781424,0.00005538368,0.00002348859,0.0001564161,0.00003828676],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001177107,0.0002161958,0.4656486,0.0002700647,0.0001764711,0.0003026567,0.0003194975,0.03249681,0.2474431,0.0002291482,0.00150163,0.2502187],"study_design_scores_gemma":[0.00003852702,0.0004396969,0.8329183,0.00003924245,0.0001180965,0.0002295346,0.0005352299,0.1396361,0.02348133,0.0002187856,0.002300999,0.00004424616],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.982852,0.0009091689,0.009964466,0.00009947352,0.00002142568,0.00004483901,0.0009763939,0.0003821168,0.00474995],"genre_scores_gemma":[0.9853404,0.0005452347,0.0124936,0.0000677073,0.00001816061,0.00001935168,0.0005997533,0.00001163074,0.0009040628],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.04546307,"threshold_uncertainty_score":0.09039688,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04446454223424853,"score_gpt":0.2427005746492835,"score_spread":0.198236032415035,"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."}}