Bibliographic record
Abstract
Recent publications have evaluated topostrophy, τ ≡ f × V · ∇ D where is f Coriolis, V is velocity, and ∇ D is gradient of total depth, as a means of comparing models' circulations. Some results are striking. Comparing four global models, two with modest grid size and two with fine grids, Merryfield and Scott (2007) show that finer grid models are characterized by more positive τ , especially at greater depths and higher latitudes. Among nine Arctic Ocean models, Holloway et al. (2007) find τ in three of the models quite distinct from the other six, a result shown to depend upon subgrid eddy parameterization. From different choices of numerical method within the same model, Penduff et al. (2007) show that improved numerical representations support more positive τ . Can these model results be compared with observations? A global compilation has been prepared from 17120 current meter records, spanning 83087 current meter‐months. The compilation tends to confirm modeling progress achieved by finer resolution, improved parameterizations and better representations. A new characterization of global ocean circulation emerges, with suggestive dynamical insights.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".