{"id":"W2792586624","doi":"10.1007/s00382-018-4142-2","title":"Impact of dynamic vegetation phenology on the simulated pan-Arctic land surface state","year":2018,"lang":"en","type":"article","venue":"Climate Dynamics","topic":"Climate change and permafrost","field":"Earth and Planetary Sciences","cited_by":11,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Victoria; Environment and Climate Change Canada; McGill University; Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Permafrost; Environmental science; Vegetation (pathology); Climatology; Albedo (alchemy); Arctic; Phenology; Climate change; Climate model; Global warming; Atmospheric sciences; Physical geography; Geology; Ecology; Geography; 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":[0.0007466481,0.0005994079,0.0004435877,0.0002809506,0.0007194531,0.001090636,0.0005964453,0.001430989,0.001935404],"category_scores_gemma":[0.002327404,0.0004273788,0.0008025002,0.0004333635,0.0006212851,0.0006831373,0.0004729912,0.0007058622,0.0002345145],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001234552,"about_ca_system_score_gemma":0.001373404,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.08932341,"about_ca_topic_score_gemma":0.04933033,"domain_scores_codex":[0.9997841,0.00006380153,0.00001179519,0.00005887401,0.00002136719,0.00006019181],"domain_scores_gemma":[0.9993219,0.0003658227,0.00004572031,0.00004940245,0.0001140543,0.0001031548],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003734937,0.00009118452,0.03704415,0.00003484761,0.0001242763,0.000096594,0.00004507577,0.9564682,0.002591087,0.0004927734,0.0005432193,0.002095047],"study_design_scores_gemma":[0.0001369328,0.0001254805,0.03542447,0.0000121295,0.00007537813,0.00004593001,0.00008755756,0.9615898,0.001408832,0.0002539411,0.0008058062,0.00003370225],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9958792,0.0001114878,0.001062502,0.0002390172,0.00006909873,0.000007170861,0.001082332,0.00009299767,0.001456254],"genre_scores_gemma":[0.9987879,0.00004423125,0.0002939309,0.00003498546,0.000007634487,0.000007327549,0.0005372603,0.00001872023,0.0002679801],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08932341,"threshold_uncertainty_score":0.1776069,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02285322498571646,"score_gpt":0.2780139506332184,"score_spread":0.2551607256475019,"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."}}