{"id":"W3206556723","doi":"10.3390/rs13204063","title":"Dynamics of Vegetation Greenness and Its Response to Climate Change in Xinjiang over the Past Two Decades","year":2021,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":41,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"National Key Research and Development Program of China; Zhejiang Provincial Ten Thousand Plan for Young Top Talents; Recruitment Program of Global Experts","keywords":"Climate change; Normalized Difference Vegetation Index; Arid; Vegetation (pathology); Physical geography; Environmental science; Precipitation; Climatology; Trend analysis; Geography; Ecology; Geology; Meteorology","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":[0.000454743,0.0001240058,0.0001503283,0.00004394884,0.00009156758,0.00003446445,0.00006660661,0.0000644773,0.000004594508],"category_scores_gemma":[0.0001433362,0.00009309202,0.0000317541,0.0005674578,0.00005714197,0.0001220782,0.000192291,0.000137483,0.00001851098],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001729688,"about_ca_system_score_gemma":0.000006520248,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003078858,"about_ca_topic_score_gemma":0.00400714,"domain_scores_codex":[0.9987864,0.0002527272,0.0001973408,0.0002785641,0.0002331224,0.0002517936],"domain_scores_gemma":[0.9994718,0.0001630054,0.00008310295,0.0001985608,0.00002470469,0.00005878948],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"observational","study_design_scores_codex":[0.0002162792,0.00002626903,0.008725332,0.00008979909,0.00001313758,0.0002722144,0.008840846,0.006996884,0.5505844,0.00008707122,0.00002707272,0.4241207],"study_design_scores_gemma":[0.0002524617,0.00001848025,0.5980439,0.0003164066,0.00001287452,0.0001539354,0.0003269427,0.3947006,0.005797794,0.0001353144,0.00008332053,0.0001579892],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9960904,0.000137061,0.0002853623,0.002642964,0.0000973291,0.0001985722,0.000002156197,0.00001635578,0.0005298232],"genre_scores_gemma":[0.9944773,0.0000568389,0.005059427,0.0002933293,0.00005232703,1.181259e-8,0.000003106232,0.00001452917,0.00004308575],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5893185,"threshold_uncertainty_score":0.3796183,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01289626508464656,"score_gpt":0.2572510631077881,"score_spread":0.2443547980231415,"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."}}