{"id":"W1987561669","doi":"10.3390/rs6043263","title":"Changes in Vegetation Growth Dynamics and Relations with Climate over China’s Landmass from 1982 to 2011","year":2014,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":152,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of British Columbia","funders":"National Key Research and Development Program of China; Goddard Space Flight Center; National Natural Science Foundation of China; Asia-Pacific Network for Sustainable Forest Management and Rehabilitation; National Aeronautics and Space Administration; Chinese Academy of Sciences; National Science Foundation","keywords":"Normalized Difference Vegetation Index; Advanced very-high-resolution radiometer; Environmental science; Climatology; Precipitation; Vegetation (pathology); China; Physical geography; Climate change; Satellite; Geography; Atmospheric sciences; Meteorology; Oceanography; Geology","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0003608371,0.0003482138,0.0001841674,0.001011797,0.0003545872,0.0004066743,0.0002110679,0.0002017003,0.000478354],"category_scores_gemma":[0.0005974637,0.000159276,0.0003311769,0.001443801,0.0002870798,0.0004151038,0.0004314925,0.0002290188,0.00009810342],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009997174,"about_ca_system_score_gemma":0.0005653183,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05205734,"about_ca_topic_score_gemma":0.1055691,"domain_scores_codex":[0.999856,0.00001190776,0.00001628178,0.00004974641,0.00002659586,0.00003936342],"domain_scores_gemma":[0.9994938,0.00004933942,0.0002214107,0.00003916556,0.0001091564,0.00008717123],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00002817132,0.00001269223,0.9927294,0.00001830414,0.0000438427,0.0001013876,0.0002843863,0.0006882187,0.0008524541,0.00005206484,0.0002600999,0.004929008],"study_design_scores_gemma":[4.81946e-7,0.000003364976,0.9994484,0.000001323444,0.000003877514,0.00001413779,0.00004084493,0.0002963665,0.00004220318,0.00000500616,0.0001422699,0.000001702599],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9989574,0.0001045977,0.00005277928,0.0000466939,0.000003465153,0.000002245473,0.0005275508,0.000006207787,0.0002991644],"genre_scores_gemma":[0.998666,0.0001082334,0.00006866709,0.00001663716,0.000008674519,0.000005313146,0.0008505844,0.000001589473,0.0002741473],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05205734,"threshold_uncertainty_score":0.1035087,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.003523196317004348,"score_gpt":0.1850254416508229,"score_spread":0.1815022453338185,"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."}}