{"id":"W4210548402","doi":"10.3390/rs14030698","title":"Deep Learning for Vegetation Health Forecasting: A Case Study in Kenya","year":2022,"lang":"en","type":"article","venue":"Remote Sensing","topic":"Hydrology and Drought Analysis","field":"Environmental Science","cited_by":46,"is_retracted":false,"has_abstract":true,"ca_institutions":"Mila - Quebec Artificial Intelligence Institute","funders":"Natural Environment Research Council; Engineering and Physical Sciences Research Council; Institute for Catastrophic Loss Reduction; Sight Research UK; Lloyd's Register; Alan Turing Institute; Lloyd's Register Foundation","keywords":"Arid; Vegetation (pathology); Baseline (sea); Climatology; Environmental science; Surface runoff; Meteorology; Environmental resource management; Geography; Geology; Ecology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"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":[0.0005143792,0.0006545606,0.0002564951,0.0004014349,0.0008016889,0.0004073018,0.000587415,0.00129098,0.001284976],"category_scores_gemma":[0.00137945,0.0001667814,0.0002751227,0.000599281,0.0003917028,0.0006514855,0.0003570937,0.0006840165,0.0001617092],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001620493,"about_ca_system_score_gemma":0.001011115,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.08236697,"about_ca_topic_score_gemma":0.1310313,"domain_scores_codex":[0.9998902,0.00003001916,0.000008307989,0.00001985891,0.00001991522,0.00003168999],"domain_scores_gemma":[0.9995235,0.0003176345,0.00003274341,0.00001524103,0.00006520505,0.00004564191],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008096586,0.0008498334,0.06480921,0.0004671938,0.0001336567,0.00811623,0.0008573699,0.8144591,0.00531439,0.004417931,0.01104468,0.08872066],"study_design_scores_gemma":[0.000104754,0.0002804564,0.02742437,0.00007182071,0.0000451045,0.0003750603,0.001178946,0.9612674,0.003176854,0.002097761,0.003931732,0.00004578692],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9861569,0.0006273261,0.005725237,0.001890708,0.00004485197,0.00008897712,0.0009720653,0.0001005471,0.004393264],"genre_scores_gemma":[0.9920013,0.0003091284,0.005482979,0.00008752576,0.00001616938,0.00002657941,0.0004016808,0.000008652606,0.001665996],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.08236697,"threshold_uncertainty_score":0.1637751,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02937250579575604,"score_gpt":0.2806530265360655,"score_spread":0.2512805207403094,"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."}}