MODELING AND UNDERSTANDING GROUNDWATER CONTAMINATION CAUSED BY CYANOTOXINS FROM HARMFUL ALGAL BLOOMS IN LAKE ERIE
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it