{"id":"W3165898521","doi":"10.3390/rs13112085","title":"Rapid Ecosystem Change at the Southern Limit of the Canadian Arctic, Torngat Mountains National Park","year":2021,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Climate change and permafrost","field":"Earth and Planetary Sciences","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"Parks Canada; Memorial University of Newfoundland; Queen's University; University of Waterloo","funders":"University of Waterloo; Queen's University; Parks Canada","keywords":"Shrub; Land cover; Environmental science; National park; Physical geography; Vegetation (pathology); Climate change; Geography; Plant cover; Land use; Ecology; Canopy","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":[0.0003895413,0.00022739,0.0001485143,0.0005965471,0.001837399,0.001134627,0.0005093871,0.0002046193,0.0007273271],"category_scores_gemma":[0.0007932531,0.0001345989,0.0002979343,0.001090929,0.0004947732,0.0003310614,0.0004439443,0.0003219859,0.0000666162],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.01304283,"about_ca_system_score_gemma":0.0146373,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.990474,"about_ca_topic_score_gemma":0.9968676,"domain_scores_codex":[0.9998049,0.00002135372,0.000005674319,0.00004305966,0.00006827211,0.00005671493],"domain_scores_gemma":[0.9997225,0.00002367534,0.00005037987,0.0000117939,0.0001252677,0.00006626883],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00009103242,0.00005731026,0.9591516,0.00007170898,0.0001197067,0.0002919033,0.0009947098,0.0108948,0.001764154,0.001061455,0.002996175,0.02250547],"study_design_scores_gemma":[0.000007560007,0.00001486904,0.9797239,0.00002902041,0.00004766734,0.00005710848,0.001512414,0.01481759,0.0001953372,0.0001775027,0.00340003,0.00001689001],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9956532,0.0003792386,0.0003199157,0.0003507214,0.00001235374,0.00001238445,0.00122517,0.00002432641,0.002022678],"genre_scores_gemma":[0.9978104,0.0002589903,0.0006249447,0.0000371461,0.000003818343,0.000005072021,0.0006304479,0.000004454691,0.0006247732],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01304283,"threshold_uncertainty_score":0.0946328,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06975024934503966,"score_gpt":0.2302398854004582,"score_spread":0.1604896360554185,"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."}}