{"id":"W3215841386","doi":"10.1007/s00180-023-01338-4","title":"Spatial correlation in weather forecast accuracy: a functional time series approach","year":2023,"lang":"en","type":"article","venue":"Computational Statistics","topic":"Insurance, Mortality, Demography, Risk Management","field":"Social Sciences","cited_by":4,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada; United States Agency for International Development; David R. Atkinson Center for a Sustainable Future , Cornell University; Xerox; National Science Foundation","keywords":"Heteroscedasticity; Autocorrelation; Autoregressive model; Econometrics; Autoregressive conditional heteroskedasticity; Variance (accounting); Series (stratigraphy); Time series; Spatial correlation; Functional data analysis; Statistics; Conditional variance; Mathematics; Computer science; Meteorology; Geography; Economics; Volatility (finance); Geology","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006455725,0.0001266788,0.0001478698,0.0002436796,0.0004135693,0.0001045912,0.000138943,0.00007111749,0.0003579099],"category_scores_gemma":[0.0002791053,0.0001433696,0.00004429677,0.0007782383,0.0002436527,0.0002367291,0.00005361405,0.0001282033,0.0005294923],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001245473,"about_ca_system_score_gemma":0.0001626178,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008445905,"about_ca_topic_score_gemma":0.0008649456,"domain_scores_codex":[0.9981952,0.0001823738,0.0003099134,0.0002651573,0.0007496878,0.0002976227],"domain_scores_gemma":[0.9990287,0.0004948517,0.0001294929,0.00008959531,0.0001923668,0.00006503882],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"observational","study_design_scores_codex":[0.00007404832,0.0001722899,0.1161651,0.00003844233,0.00006028547,0.00002567451,0.004637955,0.3306237,0.000001385842,0.4906231,0.03412114,0.02345685],"study_design_scores_gemma":[0.0003288375,0.0000220472,0.6303374,0.000008688637,0.00001233167,9.944031e-7,0.0005097246,0.2581419,1.636961e-7,0.1026667,0.007805917,0.000165286],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.03014974,0.00002861162,0.94238,0.0005770465,0.0008945954,0.0007507793,0.0005245123,0.0002797699,0.02441497],"genre_scores_gemma":[0.9615408,0.00002909157,0.03337537,0.0001204372,0.0003454252,0.00007683896,0.001920312,0.00002526935,0.002566461],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9313911,"threshold_uncertainty_score":0.6805729,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02784569056229829,"score_gpt":0.2848721261267276,"score_spread":0.2570264355644293,"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."}}