Biogeochemical cycling in restored and unrestored coastal wetlands of Lake Ontario
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Wetlands provide many ecosystem services, including carbon burial and nutrient pollution remediation from excessive anthropogenic inputs. In response to loss and degradation of Laurentian Great Lake coastal wetlands, restoration efforts along the southern shore of Lake Ontario in recent years aimed to improve habitat quality and biodiversity. It is currently unclear if these restorations impacted biogeochemical processes of key nutrients such as nitrogen (N), phosphorus (P), and carbon. To determine if restoration improved nutrient retention from terrestrial inputs and what factors drive dissolved organic matter (DOM) composition, I analyzed water chemistry, watershed land use, and hydrological connectivity of four restored and four unrestored wetlands over the growing season of 2017 under storm and base flows. All wetlands showed nutrient retention abilities with lower N and P concentrations than their tributaries, but unrestored wetlands had significantly higher nutrient loading and reduction. DOM composition was not significantly affected by restoration, but restored wetlands contained higher concentrations of DOM. N was best removed in the spring, and P was best removed in the fall, with some variation across flow condition. DOM concentration was higher during storm flow and DOM character increased in microbial-like components from spring to fall. DOM, N, and P concentrations correlated positively with agricultural land use across wetlands. The control of watershed-scale land use on downstream water quality coupled with unusually wet conditions of 2017 when these wetlands were sampled may explain why small-scale recent habitat restoration did play a more significant role in N, P, and DOM dynamics. Studying biogeochemistry in wetlands under finer spatial and temporal resolutions over longer time periods may contribute information for future restorative efforts and management practices imposed on Great Lakes coastal wetlands to preserve their health and value.
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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) | 0.001 | 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