Calibration versus computation: Comparison between 1D and 3D phytoplankton simulations in western Lake Erie
Bibliographic record
Abstract
Abstract Numerical models are commonly used tools to simulate hydrodynamics and water quality of lakes. Model dimensionality (0D, 1D, 2D, or 3D) requires different simplification levels of physical-biogeochemical processes, computational power and calibration strategies and metrics against observations. To investigate these modelling considerations, the 1D (vertical) Aquatic Ecosystem Dynamics – General Lake Model and the 3D Aquatic Ecosystem Model were applied to western Lake Erie in 2008 and 2011-14. The performance of the models was evaluated by comparing the simulations against observations of water temperature, total phosphorus, orthophosphate, nitrate, total chlorophyll-a and cyanobacteria at three stations located along a transect from the Maumee River mouth to mid-basin, as well as to the basin-averaged cyanobacteria index. The 3D model showed better skill in qualitatively reproducing seasonal and spatial variations of nutrients and phytoplankton and had lower average root-mean-square error, especially through the algal plume near the Maumee River mouth. However, the horizontally averaged 1D model performed better in qualitatively capturing the cyanobacteria bloom years, as this model was extensively calibrated to basin-average values. We conclude that models should be selected and calibrated to provide the required decision support information, rather than the highest resolution or lowest error metrics at discrete sites.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".