{"id":"W2076009596","doi":"10.5539/esr.v1n2p279","title":"Characterizing Vegetation Response to Climatic Variations in Hovsgol, Mongolia Using Remotely Sensed Time Series Data","year":2012,"lang":"en","type":"article","venue":"Earth Science Research","topic":"Rangeland Management and Livestock Ecology","field":"Environmental Science","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Saskatchewan","funders":"","keywords":"Normalized Difference Vegetation Index; Vegetation (pathology); Growing season; Phenology; Climate change; Physical geography; Environmental science; Enhanced vegetation index; Ecosystem; Climatology; Geography; Ecology; Vegetation Index; Geology","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.0003968901,0.0001488689,0.00012077,0.0005428686,0.0002105176,0.0003672589,0.0002128871,0.0002547488,0.0002443975],"category_scores_gemma":[0.0003981428,0.0001073605,0.0001699098,0.0006002917,0.0001435576,0.0002734582,0.0001824971,0.00009215501,0.00004606225],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007084332,"about_ca_system_score_gemma":0.0004919373,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06743243,"about_ca_topic_score_gemma":0.1214105,"domain_scores_codex":[0.9998721,0.00003615281,0.00001351829,0.00003237529,0.00001319196,0.00003269721],"domain_scores_gemma":[0.9996884,0.00007077124,0.0001117027,0.00003154608,0.00005483034,0.00004287338],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00005332343,0.00003733537,0.9856449,0.00003829642,0.0000800549,0.0001227457,0.0006624729,0.002433185,0.003059923,0.00005463087,0.0002032199,0.007609931],"study_design_scores_gemma":[0.000002594831,0.00002044722,0.9968645,0.000005449511,0.0000153297,0.00002657016,0.0005153957,0.002155827,0.0001688724,0.00001190834,0.0002102816,0.000002759708],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9996452,0.00003676366,0.00005453241,0.000009390684,7.906372e-7,0.000001774721,0.0001803747,0.000002230557,0.00006883433],"genre_scores_gemma":[0.9990808,0.00004046197,0.0002015067,0.000005807742,0.000001574922,0.000005855738,0.0005840524,0.000001000934,0.00007891266],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06743243,"threshold_uncertainty_score":0.1340799,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1088384664589506,"score_gpt":0.3774516543306355,"score_spread":0.2686131878716849,"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."}}