{"id":"W3190761477","doi":"10.1002/qj.4139","title":"Numerical discretization causing error variance loss and the need for inflation","year":2021,"lang":"en","type":"article","venue":"Quarterly Journal of the Royal Meteorological Society","topic":"Meteorological Phenomena and Simulations","field":"Earth and Planetary Sciences","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada","funders":"","keywords":"Discretization; Covariance; Mathematics; Data assimilation; Variance inflation factor; Covariance matrix; Applied mathematics; Propagation of uncertainty; Covariance function; Variance (accounting); Advection; Statistics; Mathematical analysis; Meteorology; Physics; Regression analysis","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":[0.001019729,0.0001161134,0.0002782753,0.000008274212,0.0004892029,0.0001080027,0.0001778647,0.0001114745,0.000259633],"category_scores_gemma":[0.0003544092,0.00005042379,0.0003084546,0.0001958468,0.0002987457,0.0001230188,0.00001112035,0.0002704653,0.000001603557],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007725806,"about_ca_system_score_gemma":0.00003574646,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002537408,"about_ca_topic_score_gemma":0.00001142643,"domain_scores_codex":[0.99854,0.000411895,0.0004239951,0.0001535757,0.0002604586,0.00021003],"domain_scores_gemma":[0.9980064,0.001274862,0.0003226384,0.0001298263,0.0001754814,0.00009079857],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"observational","study_design_scores_codex":[0.003237642,0.0002969856,0.3359014,0.00008771593,0.0007608513,0.00003234885,0.005592383,0.5576335,0.0004263975,0.02469628,0.001385469,0.06994907],"study_design_scores_gemma":[0.002638757,0.001062272,0.4444025,0.0000170045,0.0001682463,0.00006173748,0.0005578227,0.3874459,0.00002111846,0.162298,0.001150978,0.0001756653],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8827155,0.0009852557,0.1050026,0.01027127,0.0004846592,0.0002690259,0.00001475815,0.0000121132,0.0002447434],"genre_scores_gemma":[0.9944975,0.00001082488,0.004130349,0.001019299,0.0002453919,0.000001404076,0.000006590425,0.000002435061,0.00008623342],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1701875,"threshold_uncertainty_score":0.3762603,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01799586104394842,"score_gpt":0.2359253781348423,"score_spread":0.2179295170908938,"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."}}