{"id":"W2052981083","doi":"10.1061/(asce)1084-0699(2004)9:2(79)","title":"Orographic Precipitation Modeling with Multiple Linear Regression","year":2004,"lang":"en","type":"article","venue":"Journal of Hydrologic Engineering","topic":"Climate variability and models","field":"Environmental Science","cited_by":67,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Longitude; Elevation (ballistics); Precipitation; Latitude; Linear regression; Orography; Orographic lift; Environmental science; Climatology; Physical geography; Geology; Meteorology; Geography; Statistics; Mathematics; Geodesy","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0003369862,0.00009217205,0.0001302191,0.00005731695,0.00004208518,0.000009768087,0.0001086269,0.0000554944,0.00003157576],"category_scores_gemma":[0.00008457624,0.0000602596,0.00005391251,0.0001456551,0.00002210102,0.0002646985,0.0000323767,0.000194702,0.000005774385],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008248321,"about_ca_system_score_gemma":0.000007017488,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000211656,"about_ca_topic_score_gemma":0.000009481771,"domain_scores_codex":[0.9992984,0.00001126944,0.0002339796,0.0001005507,0.0002051624,0.000150653],"domain_scores_gemma":[0.9996856,0.0000389128,0.0001022181,0.00009086367,0.00001434851,0.00006804212],"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":[0.00003519684,0.00004245981,0.004001617,0.000007769477,0.000007370516,0.00001408002,0.0001937073,0.9830492,0.01251689,0.00002636911,0.000001143441,0.0001041754],"study_design_scores_gemma":[0.0006194614,0.0003049498,0.001007002,0.00006177049,0.00001619472,0.00009400924,0.00002219959,0.9963694,0.0007492469,0.0006090449,0.00005507281,0.0000916512],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8516218,0.00002965331,0.1479997,0.0001270532,0.00006196288,0.00006766302,3.365087e-7,0.00002127977,0.00007055821],"genre_scores_gemma":[0.9659281,0.00002847693,0.03396662,0.0000243012,0.00003914877,0.00000236695,6.457407e-7,0.000008277793,0.000002067401],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1143063,"threshold_uncertainty_score":0.2457315,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01322370840040637,"score_gpt":0.2137005765415085,"score_spread":0.2004768681411021,"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."}}