Bibliographic record
Abstract
Abstract The North American Ensemble Forecasting System (NAEFS) is the combination of two Ensemble Prediction Systems (EPS) coming from two operational centers: the Canadian Meteorological Centre (CMC) and the National Centers for Environmental Prediction (NCEP). This system provides forecasts of up to 16 days and should improve the predictability skill of the probabilistic system, especially for the second week. First, a comparison between the two components of the NAEFS is performed for several atmospheric variables with “objective” verification tools developed at CMC [i.e., the continuous ranked probability score (CRPS) and its reliability-resolution decomposition, the reduced centered random variable, and confidence intervals estimated with bootstrap methods]. The CMC system is more reliable, especially because of a better ensemble dispersion, while the NCEP system has better probabilistic resolution. The NAEFS, compared to the CMC and NCEP EPSs, shows significant improvements both in terms of reliability and resolution. The predictability has been improved by 1–2 forecast days in the second week. That improvement is not only a result of the increased ensemble size in the EPS—from 20 members to 40 in the present case—but also to the combination of different models and initial condition perturbations. By randomly mixing members from the CMC and NCEP systems in a 20-member EPS, an intrinsic skill improvement of the system is observed.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".