Algal bloom transport in Lake Erie using remote sensing and hydrodynamic modelling: Sensitivity to buoyancy velocity and initial vertical distribution
Bibliographic record
Abstract
In this study, we simulate three-dimensional transport of algal blooms in Lake Erie using a combination of remote sensing and hydrodynamic modelling. The remote sensing algorithms use data from the Sentinel-3 OLCI satellite sensor to derive chlorophyll-a concentration from cyanobacteria blooms in Lake Erie. The derived chlorophyll-a concentration initializes an algal bloom transport model driven by the lake component of the Water Cycle Prediction System for the Great Lakes, a system of coupled atmosphere-lake-hydrological models operated out of Environment and Climate Change Canada. The bloom is modelled as Microcystis aeruginosa, a buoyant species that is often dominant in harmful algal blooms in western Lake Erie. Short-term (a few days) predictions of algal bloom transport from July 27 to October 8, 2017 are modelled in both Eulerian and Lagrangian frameworks. The Eulerian framework is used to evaluate the sensitivity of model results to the initial vertical distribution of the bloom. In this work, the Lagrangian framework is limited to two-dimensional surface confined particles. We use several error metrics to evaluate model predictions. We find that results are sensitive to the buoyancy velocity for cases where the bloom was initially distributed over a large portion of the water column. An initial vertical distribution selected from modelled chlorophyll-a half depth shows the highest accuracy for the entire range of buoyancy velocities tested. We also find that the Pierce skill score is difficult to interpret, particularly in cases where bloom intensity is greatly overpredicted by the model.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".