Multi-Year Simulation of Western Lake Erie Hydrodynamics and Biogeochemistry to Evaluate Nutrient Management Scenarios
Bibliographic record
Abstract
During the 1970s, harmful cyanobacteria (HFCB) were common occurrences in western Lake Erie. Remediation strategies reduced total P loads and bloom frequency; however, HFCB have reoccurred since the mid-1990s under increased system stress from climate change. Given these concurrent changes in nutrient loading and climate forcing, there is a need to develop management tools to investigate historical changes in the lake and predict future water quality. Herein, we applied coupled one-dimensional hydrodynamic and biogeochemical models (GLM–AED) to reproduce water quality conditions of western Lake Erie from 1979 through 2015, thereby removing the obstacle of setting and scaling initial conditions in management scenarios. The physical forcing was derived from surface buoys, airports, and land-based stations. Nutrient loads were reconstructed from historical monitoring data. The root-mean-square errors between simulations and observations for water levels (0.36 m), surface water temperature (2.5 °C), and concentrations of total P (0.01 mg L−1), PO4 (0.01 mg L−1), NH4 (0.03 mg L−1), NO3 (0.68 mg L−1), total chlorophyll a (18.74 μg L−1), chlorophytes (3.94 μg L−1), cyanobacteria (12.44 μg L−1), diatoms (3.17 μg L−1), and cryptophytes (3.18 μg L−1) were minimized using model-independent parameter estimation, and were within literature ranges from single year three-dimensional simulations. A sensitivity analysis shows that 40% reductions of total P and dissolved reactive P loads would have been necessary to bring blooms under the mild threshold (9600 MTA cyanobacteria biomass) during recent years (2005–2015), consistent with the Annex 4 recommendation. However, these would not likely be achieved by applying best management practices in the Maumee River watershed.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".