{"id":"W4292457519","doi":"10.1111/geb.13583","title":"Divergent responses of autumn vegetation phenology to climate extremes over northern middle and high latitudes","year":2022,"lang":"en","type":"article","venue":"Global Ecology and Biogeography","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":54,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université du Québec à Montréal","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Biome; Phenology; Temperate climate; Taiga; Boreal; Latitude; Environmental science; Climate change; Climatology; Vegetation (pathology); Evergreen; Atmospheric sciences; Ecology; Physical geography; Ecosystem; Geography; Biology; Geology","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":[0.0002835387,0.0001507579,0.0001565672,0.0003623686,0.0001507718,0.0002994447,0.00009464304,0.0001272891,0.0007342192],"category_scores_gemma":[0.0004503226,0.00007968228,0.0001685814,0.0003180387,0.0001179572,0.000162457,0.0002249509,0.0001313906,0.0001128166],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000142812,"about_ca_system_score_gemma":0.000108253,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003940055,"about_ca_topic_score_gemma":0.007659627,"domain_scores_codex":[0.9998711,0.00002718256,0.000007848103,0.00004895302,0.00001748983,0.00002737888],"domain_scores_gemma":[0.999676,0.00005807959,0.0001049231,0.00002548887,0.00007401422,0.00006160386],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0002872387,0.00003083749,0.9732703,0.0000438637,0.0001142851,0.0000826757,0.0003431664,0.0006019113,0.01719097,0.00007503761,0.000258695,0.00770086],"study_design_scores_gemma":[9.270465e-7,0.000007336826,0.9995738,0.000001201947,0.000003047447,0.00001054426,0.00004524285,0.0001623126,0.0001025717,0.000009750259,0.00008263113,7.169449e-7],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.999146,0.00008727829,0.0001264663,0.00001251589,0.000002748632,0.000001873277,0.0002709358,0.000006038333,0.0003462311],"genre_scores_gemma":[0.9995812,0.00002299951,0.00005658399,0.000005883565,0.000002874797,0.000002605056,0.0002346668,0.000001840528,0.00009135219],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.003940055,"threshold_uncertainty_score":0.007834196,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007061280349676705,"score_gpt":0.2028521384802909,"score_spread":0.1957908581306141,"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."}}