Implementation of a 3D ocean model to understand upland lake wind-driven circulation
Bibliographic record
Abstract
A community numerical ocean model is used to extend the understanding of wind-driven circulation in small upland lakes. A 3D model of a case study lake (Llyn Conwy, Wales, UK) is calibrated against measured velocity profiles via adjustment of the bottom roughness coefficient. Validation against a separate set of measured velocity profiles confirms the ability of the model to resolve key features of the flow field. Sensitivity analysis shows that the velocity field responds rapidly to changes in the wind forcing. Analysis of the gross circulation using Empirical Orthogonal Functions reveals a persistent two-gyre circulation pattern in the upper half layer of the water column driven by the interaction of wind and bathymetry. At the bottom, the flow is characterised by locally strong currents and analysis of vertical circulation over short time scales shows strong currents in the deepest parts of the lake basin and the responsiveness of the water column to changes in wind speed and direction. Even in small lakes, the assumption of uniform wind stress across the water surface is not always justified and topographic sheltering or other catchment roughness effects give rise to heterogeneity in the wind field. An idealized experiment for the case study lake shows that differences in circulation emerge if the wind stress is allowed to vary across the lake. Energetic wind forcing in upland areas can drive an energetic lake circulation that has important implications for mixing and sediment dynamics. 3D numerical modelling of wind-driven circulation should be more widely used to provide insights into physical limnology to support a wide range of ecological, biogeochemical and palaeoenvironmental studies.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".