{"id":"W2792661426","doi":"10.5539/sar.v7n2p46","title":"Drought Monitoring in the Dry Zone of Myanmar using MODIS Derived NDVI and Satellite Derived CHIRPS Precipitation Data","year":2018,"lang":"en","type":"article","venue":"Sustainable Agriculture Research","topic":"Climate variability and models","field":"Environmental Science","cited_by":23,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Normalized Difference Vegetation Index; Precipitation; Environmental science; Vegetation (pathology); Climatology; Satellite; Agriculture; Physical geography; Climate change; Meteorology; Geography; Geology","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002261349,0.0001274828,0.00008806948,0.0004527967,0.0001410598,0.0002085497,0.0001329755,0.0001044191,0.0002454516],"category_scores_gemma":[0.0003076817,0.00008101128,0.0001356089,0.0004268108,0.00006068068,0.0002940278,0.0002604277,0.0001175801,0.00005532297],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002117838,"about_ca_system_score_gemma":0.0001981959,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009079093,"about_ca_topic_score_gemma":0.0188013,"domain_scores_codex":[0.9999293,0.00002402568,0.000006714355,0.00001781702,0.00001004622,0.00001216649],"domain_scores_gemma":[0.9998899,0.00001630543,0.00004959234,0.00001172539,0.00002037569,0.00001217319],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00008851701,0.00008976813,0.9575091,0.00005537199,0.00009731571,0.0001747487,0.0003666894,0.004026751,0.009560732,0.0001755618,0.0005281629,0.02732727],"study_design_scores_gemma":[0.000005356858,0.00004581569,0.9878086,0.00001024098,0.00002353532,0.00004120972,0.0003043035,0.009977925,0.0009798156,0.00004563737,0.0007526591,0.000004854041],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9990391,0.00004980626,0.0002762766,0.00002942886,0.000001522233,0.000005716628,0.0003289766,0.000008154287,0.0002610809],"genre_scores_gemma":[0.9982805,0.00007160576,0.0008602502,0.000005741333,0.000002801907,0.00001110924,0.000630965,0.000001166973,0.0001358301],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009079093,"threshold_uncertainty_score":0.01805252,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08255802997215186,"score_gpt":0.3512013610394993,"score_spread":0.2686433310673474,"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."}}