Bibliographic record
Abstract
Wind is generated from left to right by an imposed constant horizontal pressure gradient. The initial wind field is disturbed by small random variations so as to produce a turbulent field. Withouth the perturbations, a viscous solution would be found. The numerical resolution technique used is based on finite differences, applied to a structured mesh. The Continuity and Navier-Stokes equations are solved with the well-known half time-step method, in which the Poisson equation is solved over the entire domain at each time iteration. As of 17 March 2022, the code version is DNS_2D_for_Teaching-v1.0.0. The code is written in C language. A GUI (Graphical User Interface) is available as an executable file "sdiapp.exe" that can be run under most versions of Microsoft Windows. Please just make sure to check the 'graph' box before clicking on the launch button, to have the visual experience. On the GUI, two graphs give an overview of the real time simulation. The top graph shows the 2D (x,z) vorticity, while the bottom graph shows the wind speed. The colour bars are not shown, but they are classical tables in which blue means small values, while red colours denote large values. The authors of this code version are Francis Vivat (LATMOS UMR CNRS 8190) and Denis Bourras (MIO UMR 7294). The code is distributed freely and comes with no garantees. It was mainly designed for educational purposes. Please note that the rules of use must follow the CeCILL-C FREE SOFTWARE LICENSE AGREEMENT included in the distribution. Any return is welcomed and encouraged, please contact francis.vivat@latmos.ipsl.fr or denis.bourras@mio.osupytheas.fr. Citation: Vivat, F., & Bourras, D., (2023). DNS_2D_for_Education [Application].
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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) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.358 | 0.166 |
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".