{"id":"W2608945601","doi":"10.5194/gmd-10-2905-2017","title":"REDCAPP (v1.0): parameterizing valley inversions in air temperature data downscaled from reanalyses","year":2017,"lang":"en","type":"article","venue":"Geoscientific model development","topic":"Cryospheric studies and observations","field":"Earth and Planetary Sciences","cited_by":43,"is_retracted":false,"has_abstract":true,"ca_institutions":"Carleton University","funders":"Fundamental Research Funds for the Central Universities; National Natural Science Foundation of China; Canada Foundation for Innovation; WSL-Institut für Schnee- und Lawinenforschung SLF","keywords":"Downscaling; Surface air temperature; Environmental science; Climatology; Meteorology; Scale (ratio); Pooling; Proxy (statistics); Atmospheric sciences; Elevation (ballistics); Geology; Computer science; Mathematics; Precipitation; Statistics; Geography","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":[0.001284812,0.0008432764,0.0004267463,0.0004480437,0.0002651322,0.0006160631,0.001821966,0.0005471887,0.004349891],"category_scores_gemma":[0.002925612,0.0007890423,0.000871528,0.0006279172,0.0001895832,0.0006423157,0.0005990333,0.001025729,0.0009807377],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002803251,"about_ca_system_score_gemma":0.0008051812,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0137494,"about_ca_topic_score_gemma":0.01478696,"domain_scores_codex":[0.9998176,0.0000549143,0.00001157908,0.00005958698,0.00003279721,0.00002362414],"domain_scores_gemma":[0.9995437,0.0002007879,0.00004483355,0.00009852678,0.00008413052,0.00002800161],"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":[0.0004058038,0.0002782534,0.03529904,0.0006352317,0.0009938019,0.0002727766,0.0002872436,0.7798756,0.01724268,0.001786446,0.03079279,0.1321302],"study_design_scores_gemma":[0.00008145278,0.00003475162,0.005053854,0.00001437765,0.00003154259,0.00003427088,0.00002195567,0.9854003,0.004704281,0.000534001,0.004063462,0.00002578844],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4230299,0.0005247475,0.4336176,0.0002738227,0.0003054674,0.0004167989,0.03255225,0.1039162,0.005363262],"genre_scores_gemma":[0.5197645,0.0001584037,0.4453005,0.0001235539,0.00004823218,0.0004787612,0.02657083,0.006113121,0.001442047],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.0137494,"threshold_uncertainty_score":0.02733874,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1011621263627208,"score_gpt":0.2693605487586824,"score_spread":0.1681984223959616,"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."}}