{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0001617379,0.0001447386,0.0001475085,0.00005044991,0.0001079108,0.00004630749,0.00004842355,0.00008526615,0.00001766904],"category_scores_gemma":[0.00004163524,0.0001126733,0.00001406469,0.0001892536,0.00005208678,0.0001140229,0.00007451743,0.0001520976,0.0001047612],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001836044,"about_ca_system_score_gemma":0.000002813956,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005902012,"about_ca_topic_score_gemma":0.06529472,"domain_scores_codex":[0.9990263,0.0000738203,0.0001243378,0.000329213,0.0002021561,0.0002441655],"domain_scores_gemma":[0.9996085,0.00005702854,0.00006990387,0.0001697004,0.000009157343,0.00008569204],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0002055846,0.00007356044,0.1904569,0.00008115443,0.00005617799,0.0001163084,0.01263505,0.06096348,0.09591633,0.0005207716,0.001147853,0.6378268],"study_design_scores_gemma":[0.0002016718,0.00003017865,0.5464654,0.00009433163,0.00001094428,0.00001528344,0.00003460295,0.4520403,0.0003213431,0.0005225929,0.0001150351,0.0001482973],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9804016,0.000009053041,0.00804213,0.001926016,0.0000794147,0.0001778651,0.00000303545,0.00004806201,0.009312816],"genre_scores_gemma":[0.9524689,0.00001749664,0.04706159,0.0002302676,0.00005679229,8.914022e-9,0.00002639409,0.00001836313,0.0001201646],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6376785,"threshold_uncertainty_score":0.9517612,"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."}}