{"id":"W2971838902","doi":"10.1088/2515-7620/ab3d87","title":"The spatial-temporal distributions of controlling factors on vegetation growth in Tibet Autonomous Region, Southwestern China","year":2019,"lang":"en","type":"article","venue":"Environmental Research Communications","topic":"Remote Sensing in Agriculture","field":"Environmental Science","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Natural Resources Canada; McMaster University","funders":"National Natural Science Foundation of China; National Key Research and Development Program of China; National Aeronautics and Space Administration","keywords":"Normalized Difference Vegetation Index; Precipitation; Cru; Environmental science; Vegetation (pathology); Arid; Physical geography; Climatology; Steppe; Grassland; Climate change; Shrub; Atmospheric sciences; Geography; Ecology; Meteorology; 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":[0.0007753181,0.0001393632,0.0001547948,0.0000612585,0.0005562574,0.00004956582,0.001007592,0.00008420949,0.00005382145],"category_scores_gemma":[0.0002107979,0.00009897674,0.00007267497,0.000303771,0.0008839529,0.0001493504,0.0006073227,0.0006386461,0.0004099469],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006948046,"about_ca_system_score_gemma":0.00001769552,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002340704,"about_ca_topic_score_gemma":0.001877169,"domain_scores_codex":[0.9976959,0.0006644466,0.0003525934,0.0002670862,0.000661115,0.0003588708],"domain_scores_gemma":[0.9975393,0.00098894,0.0001432863,0.001234354,0.000009327296,0.00008477324],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00003639893,0.0004642298,0.9804009,0.000005849986,0.00001936953,0.000001588166,0.001993366,0.001462375,0.01055154,0.0009716301,0.0001495149,0.003943252],"study_design_scores_gemma":[0.0003333495,0.0001145145,0.9912578,0.00003603962,0.00000406113,0.000001877554,0.0005874002,0.003637488,0.001249853,0.0008495873,0.001818023,0.0001099942],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9921207,0.0001065094,0.0001746765,0.001678773,0.00004763415,0.0007226541,0.00002256654,0.00001771845,0.005108783],"genre_scores_gemma":[0.9989537,0.0002540272,0.000182499,0.00001215584,0.000009606123,0.00001530671,0.0001579923,0.00001500729,0.0003997179],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01085693,"threshold_uncertainty_score":0.5269175,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02671319288550656,"score_gpt":0.2825624059586526,"score_spread":0.2558492130731461,"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."}}