{"id":"W2022349500","doi":"10.1016/j.rse.2015.02.024","title":"A new satellite-based monthly precipitation downscaling algorithm with non-stationary relationship between precipitation and land surface characteristics","year":2015,"lang":"en","type":"article","venue":"Remote Sensing of Environment","topic":"Precipitation Measurement and Analysis","field":"Earth and Planetary Sciences","cited_by":195,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Toronto","funders":"National Geospatial-Intelligence Agency; Chinese Academy of Sciences; National Natural Science Foundation of China; National Aeronautics and Space Administration","keywords":"Downscaling; Precipitation; Normalized Difference Vegetation Index; Environmental science; Quantitative precipitation estimation; Satellite; Remote sensing; Algorithm; Climatology; Rain gauge; Vegetation (pathology); Meteorology; Computer science; Climate change; Geology; Geography","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004642429,0.000623402,0.001015217,0.0009768923,0.0004375784,0.0005596611,0.00115216,0.0005218439,0.002018719],"category_scores_gemma":[0.0008101885,0.000465978,0.000681946,0.001440867,0.0001556024,0.0009397424,0.0007209339,0.0006413605,0.001224481],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002141075,"about_ca_system_score_gemma":0.0009980677,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005093036,"about_ca_topic_score_gemma":0.009239269,"domain_scores_codex":[0.9996538,0.0000267998,0.00002595748,0.0001078503,0.0001599351,0.00002568959],"domain_scores_gemma":[0.9996371,0.00003904906,0.00003033025,0.00004808787,0.0002238206,0.00002157803],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002027167,0.0001610559,0.003758094,0.00009410227,0.0001804653,0.00006400616,0.00003530629,0.03728093,0.05105605,0.001338313,0.01029501,0.895534],"study_design_scores_gemma":[0.0000833955,0.00005708711,0.00591993,0.000006898361,0.00006822042,0.0001296771,0.00001296027,0.9723803,0.01111359,0.0007870352,0.009404592,0.00003631904],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02111492,0.0003326578,0.9747867,0.00008198323,0.0002500153,0.00007998486,0.0003655179,0.002016943,0.0009712529],"genre_scores_gemma":[0.06859062,0.0002515471,0.9243734,0.0001176783,0.0001795312,0.0001626701,0.001945799,0.0002319295,0.004146769],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005093036,"threshold_uncertainty_score":0.01012677,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0328635121938099,"score_gpt":0.2187170694369136,"score_spread":0.1858535572431037,"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."}}