Hydrologic and Landscape Drivers of Seepage Lake ANC Status in Northern Wisconsin, USA
Bibliographic record
Abstract
ABSTRACT In some seepage lakes, acid neutralising capacity (ANC) is regulated by precipitation chemistry and in‐lake biogeochemical processes with little influence from groundwater and catchment runoff. However, additional environmental contexts, for example, landscape position and changing precipitation dynamics, may also contribute to variability in ANC across lakes over time. We used a mixed effects model to assess the influence of changing precipitation patterns and the chemistry of hydrologic inputs on ANC in seven northern Wisconsin, USA seepage lakes from 1984 to 2018. We observed differential ANC responses across lakes to the concomitant changes in precipitation acidity and post‐drought oxidative acidity pulses from adjacent wetland soils. Although all study lakes lie within a 4 km 2 area, mean lake ANC values ranged from 32.8 ± 25.2 μeq L −1 to 71.9 ± 26.8 μeq L −1 and were inversely related to lake landscape position. Lakes with higher mean ANC also showed lower relative variability over time, suggesting that these lakes were better buffered than those with lower ANC. Mixed effects modelling explained 50% of the ANC variability across all lakes, while model explanatory power ranged from 39% to 71% when ANC was assessed within individual lakes. The range of modelled ANC accuracy highlights the complex nature of biogeochemical regulation in individual seepage lakes, despite their shared geographic setting. Our study demonstrates the utility of mixed effects modelling for repeated measures analysis, emphasising the necessity of long‐term studies to evaluate seepage lake ANC responses to changing hydrologic and biogeochemical inputs.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".