{"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":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0005753726,0.0002006688,0.0002613338,0.00007134255,0.001795853,0.00027632,0.001413979,0.00008555806,0.000505385],"category_scores_gemma":[0.0001748467,0.0001675374,0.00004376273,0.0002230573,0.0001527502,0.000535563,0.0003859343,0.0001796443,0.0001679318],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002034378,"about_ca_system_score_gemma":0.000239966,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.007093451,"about_ca_topic_score_gemma":0.04488625,"domain_scores_codex":[0.9978559,0.00003587602,0.0004010669,0.0008399036,0.0004365279,0.0004307273],"domain_scores_gemma":[0.9980019,0.00007705775,0.0001691544,0.001546696,0.0000598548,0.0001452749],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0000811668,0.0001618102,0.8156701,0.00003773366,0.0002505618,0.00006956618,0.004878541,0.06400517,0.0008612664,0.00003786487,0.07378402,0.04016225],"study_design_scores_gemma":[0.0002837234,0.000004819923,0.7333215,0.00005264287,0.00001868977,6.629267e-7,0.0004261925,0.243974,0.00005865659,0.0002981851,0.02130073,0.0002601613],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9943852,0.0003636487,0.0009304124,0.0009676059,0.001021484,0.0002359796,0.0007287444,0.00004263693,0.001324245],"genre_scores_gemma":[0.9430645,0.000131897,0.05131716,0.0003524758,0.0000503533,0.000006247593,0.00278628,0.000005636122,0.002285508],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1799688,"threshold_uncertainty_score":0.9995184,"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."}}