Scripts and datas for "An energetically and observationally constrained mesoscale parameterisation for ocean climate models".
Bibliographic record
Abstract
Scripts and datasets used for creating the results of a submitted work : R. Torres, R. Waldman, G. Madec, C. de Lavergne, R. Séférian and J. Mak: A unified energy-constrained mesoscale parameterisation for ocean climate models. (submitted in JAMES). Datas include eORCA1 mesh files (directory "mesh") and simulations output (direcotories "runs/*/output"). However, to avoid heavy archive, only 2D simulations output are provided. The post-processed 3D variables are first pre-processed for each simulations (directories "runs/*/post/post/post_averag_1995-2017"). The reference EKE of Torres et al. (2023) is provided (directory "obs/postprocessed_kinetic_energy") while other observational reference datasets have to be download by the user (e.g. World Ocean Atlas 2018, Tsujino et al. (2020) and RAPID) IPython notebooks for computing and plotting metrics are provided : james-eke-heat_budget.ipynb : plots for heat transport and global heat storage (section 4.1) james-eke-southern_ocean.ipynb : plots for Southern Ocean (section 4.2) analysis james-eke-north_atlantic.ipynb : plots for North Atlantic and Labrador Sea (section 4.3) analysis james-eke-timeseries.ipynb : plot 0D metric timeseries for simulations (including spin-up) Note however that these scripts use the author python library XOCE availbale on GitHub: https://github.com/torresr-cnrm/xoce. All the scripts have been runned using the version 0.2 of XOCE. Feel free to contact (romain.torres4@gmail.com) for any help in installing and using this library. In order to limit the size of the present repository, some datas data may be missing. Feel free to contact (romain.torres4@gmail.com) for any additional datas.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.285 | 0.162 |
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".