{"id":"W2929357616","doi":"10.1038/s41597-019-0038-1","title":"Statistically downscaled climate dataset for East Africa","year":2019,"lang":"en","type":"article","venue":"Scientific Data","topic":"Climate variability and models","field":"Environmental Science","cited_by":111,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Downscaling; Climate change; Climatology; Precipitation; Climate model; Environmental science; Scale (ratio); Representative Concentration Pathways; Projection (relational algebra); Tanzania; Geography; Environmental resource management; Physical geography; Meteorology; Cartography; Environmental planning; Computer science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":["insufficient_payload"],"category_scores_codex":[0.002124981,0.0001154244,0.0001461892,0.00002703807,0.0002524382,0.0002691058,0.001533083,0.00004166718,0.007825331],"category_scores_gemma":[0.0002077027,0.00009966756,0.00002437631,0.0002231027,0.0003552291,0.000636189,0.002145697,0.00006751803,0.007621373],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004405384,"about_ca_system_score_gemma":0.0000219721,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000360559,"about_ca_topic_score_gemma":0.0001450718,"domain_scores_codex":[0.9977742,0.0000368791,0.0002525959,0.00107066,0.0003716399,0.0004939925],"domain_scores_gemma":[0.9969206,0.0001097257,0.00006239497,0.002754027,0.00001080685,0.0001423817],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0000591276,0.000221361,0.001656363,0.00007421881,0.000008152926,0.000001692487,0.0001848517,0.0002875781,0.01255743,0.001836959,0.9798534,0.003258862],"study_design_scores_gemma":[0.0004390589,0.00003732716,0.0009015489,0.00001298553,0.00002495039,0.00000208687,0.00006070212,0.08078212,0.00005442905,0.003122824,0.9143544,0.0002075199],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"dataset","genre_gemma":"empirical","genre_scores_codex":[0.05118319,0.00002266954,0.01558782,0.000908464,0.002334068,0.001754517,0.9141075,0.00009565048,0.01400615],"genre_scores_gemma":[0.5716296,0.00001123064,0.06340162,0.0004168958,0.00009194143,0.0000831091,0.3570323,0.00005208247,0.007281165],"genre_candidate":"dataset","genre_consensus":null,"teacher_disagreement_score":0.5570751,"threshold_uncertainty_score":0.9931513,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06660062009485934,"score_gpt":0.2906167723772865,"score_spread":0.2240161522824272,"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."}}