MODELING AND UNDERSTANDING GROUNDWATER CONTAMINATION CAUSED BY CYANOTOXINS FROM HARMFUL ALGAL BLOOMS IN LAKE ERIE
Bibliographic record
Abstract
Committee co-chairCyanotoxins, which are produced and released into the surrounding water during harmful algal blooms (HABs), can severely deteriorate water quality and cause health-related issues and economic loss.HABs and cyanotoxin studies have been typically focused on the surface water domain (e.g., lakes, estuaries, and rivers), with few investigating or reporting on groundwater.This study aimed to explore whether groundwater can be contaminated by cyanotoxins (microcystins) from HABs in surface water due to surface water and groundwater interaction.Specifically, we created a 3-dimensional (3-D) MODFLOW/MT3DMS model to simulate pumping-induced reverse groundwater flow and solute transport from Lake Erie to the aquifer underneath South Bass Island in Ottawa County, Ohio.Simulation results show that, under the default setting, it took ~2 months, ~3 months and ~13 months for the water in pumping well to reach the EPA advisory levels of microcystins for detection (0.1 g/l), infants and children (0.3 g/l), and school-age children to adults (1.6 g/l), respectively.Furthermore, scenario analyses showed that higher pumping rate and higher lakebed leakance would accelerate the microcystin transport to groundwater well.Higher hydraulic conductivity, interestingly, would increase the time to reach those EPA levels due to mixing and dilution effect.The 3-D model developed in this study was capable of simulating the complex surface-water and groundwater interaction and transport processes in the Great Lakes setting.As the first of its kind, this modeling study provides insight for managing coastal groundwater aquifer and resources while dealing with the threat of HABs in the Great Lakes.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| 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".