Bibliographic record
Abstract
Nowadays,data assimilation has played an important role in research of atmosphere and ocean.Four dimension variation may be considered a better data assimilation method.But with data assimilation method developing,a new data assimilation method— ensemble Kalman filter is becoming popular.As a sequential data assimilation method,ensemble Kalman filter is similar to Kalman filter that has been presented by Kalman in 1960 but hard to apply to atmospheric data assimilation in operation for large calculating cost.Ensemble method makes Kalman filter available and has made a great progress in past ten years.After review development of data assimilation and ensemble Kalman filter,the virtue of ensemble Kalman filter is discussed.Getting a flow-dependent background error covariance may be a most attractive character of ensemble Kalman filter.Also,the problem of ensemble Kalman filter applied is discussed in this paper.Since we can just use finite ensemble in ensemble Kalman filter,simple error is unavoidable and will bring some severe problems,for instance,filter divergence.At the end,the future of ensemble Kalman filter is expected.Although no operational center has yet implemented ensemble Kalman filter,Canada has plan to do so.Besides,hybrid variation and four dimension variation may be mainstream of numeric weather prediction.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".