An Index of presenting authors is located at end of the Short Abstracts document
Bibliographic record
Abstract
Improving the combination of observations and dynamical models by data assimilation systems has underpinned many advances in our understanding of the natural environment, and forecasting ability. These improvements, coupled with the development of ever more powerful computers and more sophisticated communication systems such as the internet and the World Wide Web have also heightened expectations. As a result, society is looking for further significant benefits from applications of meteorology, oceanography and hydrology. It is therefore essential that this community continue to meet and plan the research and development of data assimilation: its fundamental theory and its application to meteorology, oceanography, hydrology and related fields. The use of data assimilation within areas such as modelling chemical species, coupled systems and the land surface raises many new issues. These questions come on top of those associated with continued efforts to meet the expectations from more established atmospheric and oceanic applications. Since the WMO accepted the challenge to oversee the development of Data Assimilation there has been tremendous developments in the relevant areas of science, both research and operational. This series of meetings from the first in Clermont-Ferrand (1990) followed by Tokyo (1995), Quebec City (1999), Prague (2005) and now Melbourne, have been an important part of showcasing these developments and reporting
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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.758 | 0.796 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".