Source water inputs and catchment characteristics regulate limnological conditions of shallow subarctic lakes (Old Crow Flats, Yukon, Canada)
Bibliographic record
Abstract
Climate variations exert rapid and strong control on the hydrology of shallow lake-rich subarctic landscapes, but knowledge of the associated effects on limnological conditions remains limited. Based on analysis of water isotope compositions and water chemistry at 56 lakes across Old Crow Flats (Yukon), a large thermokarst landscape, we assess if differences in source water inputs (snowmelt versus rainfall) affect limnological conditions during the ice-free season of 2007 and explore influences of catchment features. Results demonstrate that lakes with snowmelt-dominated source waters, situated in catchments that support tall shrub and woodland vegetation, possess significantly higher (p < 0.05) nutrient (N, P, SiO2) and dissolved organic carbon concentrations than lakes with rainfall-dominated source waters. Conversely, rainfall-dominated lakes, located in catchments dominated by dwarf shrubs and sparse vegetation, have significantly higher concentrations of major ions (Mg2+, Na+, SO42−) and pH. These limnological differences persisted throughout the ice-free season. We suggest that interaction of snowmelt with organic-rich detritus raises nutrient concentrations in snowmelt-dominated lakes and that evaporative-concentration, shoreline erosion and possibly rainfall runoff are processes that raise the ionic content of lakes with rainfall-dominated source waters. Knowledge of these relations improves the ability to anticipate limnological responses to ongoing and future climate and hydrological change in Arctic and subarctic regions.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.001 | 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".