Spatial heterogeneity in trophic status of shallow lakes on the Boreal Plain: Influence of hydrologic setting
Bibliographic record
Abstract
We used metrics of surface water and groundwater connectivity as explanatory variables in nonparametric regression models to explain the spatial heterogeneity in trophic status of shallow lakes. The concentration of chlorophyll a (Chl a) was used as an indicator of trophic status and was estimated from 17 Landsat images acquired during the end of summer (August) over a 20‐year period from 1984 to 2003. A long‐term median of Chl a was computed for each of 40 lakes on the basis of the 17‐year data set. Hydrologic metrics explained 72% of the spatial variation in Chl a. The regression tree showed that lakes with a higher concentration of calcium plus magnesium (Ca + Mg) exhibited higher Chl a than lakes with a lower Ca + Mg. We hypothesized that this trend was a result of either higher internal nutrient loading in high Ca + Mg lakes due to groundwater discharge or differences in surficial geology. Among high Ca + Mg lakes, lakes with no inflowing streams had lower Chl a, while connected lakes had higher Chl a, possibly reflecting enhanced nutrient delivery to lakes connected to the stream network. Among low Ca + Mg lakes, lakes with larger wetland cover in their drainage basins had lower Chl a, while lakes with smaller wetland cover in their drainage basins had higher Chl a, possibly reflecting differential water loading rates between small and large wetlands that lead to either Chl a concentration or dilution. These results suggest that relatively simple hydrologic metrics can be used to predict the trophic status of lakes in this area of the boreal forest.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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 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".