Effects of Agricultural Encroachment and Drought on Wetlands and Shallow Lakes in the Boreal Transition Zone of Canada
Bibliographic record
Abstract
The ecoregions along the southern edge of the Boreal Plains have experienced rapid and extensive agricultural encroachment over the last 60 years. Over 200 wetlands and shallow lakes in central and northern Alberta and northeastern British Columbia, Canada were surveyed along an agricultural encroachment gradient to investigate the effect of land use on water quality between 2005 and 2007. Study wetlands were typically small (median 29.8 ha), shallow (median 0.8 m) and highly eutrophic (median 148 μgTP∙L −1 ). Wetlands in the southern Boreal Plains were regionally variable and dynamic. Drought in 2006 caused significant increases in TP, TN, chlorophyll a (chl a ), conductivity, silica and significant decreases in maximum depth and light penetration. Increased agricultural activity within a 1.6 km buffer surrounding wetlands enhanced nutrients but not chl a concentrations or submersed aquatic vegetation (SAV) coverage. Wetlands with higher amounts of agriculture in the buffer zone tended to be shallower with decreased secchi depths. Due to shallow depths, SAV thrived even under hypereutrophic conditions with high agricultural encroachment in the buffer zone. Our study suggests that SAV has a significant role in maintaining clear water states in Boreal Transition Zone wetlands and shallow lakes through its ability to suppress concentrations of chl a and TP.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| 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 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".