Assessing vertical diffusion in a stratified lake using a three‐dimensional hydrodynamic model
Bibliographic record
Abstract
Abstract Vertical turbulent diffusivity (Kz), which can be estimated from water temperature, is a key factor in the evolution of water quality in lentic waters. In this study, we analysed the capability of a three‐dimensional hydrodynamic model (EFDC) to capture water temperature and vertical diffusivity in Lake Arendsee in the Northern German plain. Of particular interest to us is to evaluate the model performance for capturing the diffusion minimum within the metalimnion and analyse the response of the metalimnetic Kz to meteorological forcing, namely changing wind speed and warming. The comparison confirmed that the calibrated model could reproduce both stratification dynamics and vertical diffusion profiles in the lake. The model was also shown to be able to capture the duration and vertical extent of the metalimnetic diffusion minimum. The scenario results illustrate that, compared to air temperature, wind velocity appeared to be the more influential meteorological variable on the vertical exchange within the metalimnion. While increasing wind velocities mostly affected the minimum values of Kz in the metalimnion and thus led to intensified vertical exchange, the reduction of wind velocity mostly affected the depth of minimal Kz, but not its absolute value.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".