A critical examination of chemical extremes in freshwater systems
Bibliographic record
Abstract
The objectives of this thesis are to explore and identify: 1) the causative factors for \nextreme endpoints in freshwater chemistry, specifically eutrophication and \nacidification, and 2) the convergence of anthropogenic pollution, watershed \ncomposition and climate effects that could contribute to the occurrence of freshwater \nchemical extremes. Eutrophication is reviewed as a well-studied water quality issue \nthat remains relevant as a management challenge. Extra focus is given to acidification, \nquantified as a decrease in acid neutralizing capacity (ANC), because it has a strong \ninfluence on physical properties such as nutrient (i.e., phosphorus, nitrogen) \nresuspension that can potentially leading to chemical extremes. \nData from lakes in the western Great Lakes region are examined with respect to effects \nof acid inputs on in-lake ANC and pH response. Although drainage systems are \ndiscussed, special attention is paid to softwater seepage lakes as being the most \nsensitive with regards to acidification risk. The challenges of using data-intensive mass \nbalance models in lakes with intermittent sampling histories lead to development of a \nsimpler model for estimating open-water ANC in data-sparse locations. Acid input \nsources are compared as combinations of area-weighted charge balances using publicly \navailable data from long term monitoring programs. Weighted data combinations are \nthen analyzed using maximum likelihood methods suitable for use with observational \ndata. The final model correctly predicted low ANC events (ANC < 25 ?eq L-1 ) 20 out of \n24 times (R2 = 0.50 adjusted for small sample size; 168 observations), but \nunderestimates the severity of the lowest extremes. \nThree factors stand out as being strongly related to acidification risk during the open \nwater season: 1) volume of snowmelt, 2) in-lake ANC following spring turnover, and 3) pulsed runoff from associated wetland soils following drought and re-wetting events. \nRecommendations for future research focus on quantifying acid and nutrient content \nin pulsed runoff events and their impacts on freshwater systems given antecedent \nconditions in both lakes and connected wetlands.
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How this classification was reachedexpand
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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".