{"id":"W1818848341","doi":"10.1007/s00382-015-2809-5","title":"The reliability of single precision computations in the simulation of deep soil heat diffusion in a land surface model","year":2015,"lang":"en","type":"article","venue":"Climate Dynamics","topic":"Climate change and permafrost","field":"Earth and Planetary Sciences","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ouranos; Environment and Climate Change Canada","funders":"","keywords":"Rounding; Permafrost; Computer science; Machine epsilon; Climate model; Data assimilation; Discretization; Algorithm; Accuracy and precision; Single-precision floating-point format; Stability (learning theory); Reliability (semiconductor); Environmental science; Computation; Climate change; Climatology; Applied mathematics; Meteorology; Mathematics; Statistics; Geology; Machine learning","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008839049,0.00007040072,0.0001293639,0.00003492225,0.00006662287,0.00002177913,0.0001561297,0.00005223756,0.00001131375],"category_scores_gemma":[0.000101679,0.00004142974,0.00002843985,0.0002352299,0.00007827048,0.0001013765,0.00002155125,0.00008082368,0.000003212159],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001689749,"about_ca_system_score_gemma":0.00001857346,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002617678,"about_ca_topic_score_gemma":0.160529,"domain_scores_codex":[0.9990991,0.0001278513,0.0003061554,0.000113337,0.0002000526,0.0001534614],"domain_scores_gemma":[0.9988116,0.0008242854,0.00007463248,0.0001909561,0.00007128359,0.00002723243],"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":[0.0000484425,0.00003377441,0.4179548,0.00001179214,3.172995e-7,2.915316e-7,0.001378595,0.5798559,0.00001702658,0.000006980861,0.000001608778,0.0006904796],"study_design_scores_gemma":[0.0002319147,0.0000535076,0.1962704,0.00002054852,0.000003025847,5.718035e-7,0.0008259853,0.8011545,0.000002873228,0.001394136,0.000004216045,0.000038308],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9975216,0.000125409,0.0007452092,0.0001941366,0.00007262155,0.0001850543,0.0005171761,0.000004872623,0.0006339369],"genre_scores_gemma":[0.9990244,0.0001445818,0.000104662,0.00001995875,0.000006827316,4.913281e-7,0.0006938449,0.00000226975,0.000002988643],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2216844,"threshold_uncertainty_score":0.8547892,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04530183883867155,"score_gpt":0.2728811428024247,"score_spread":0.2275793039637531,"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."}}