Active layer slope disturbances affect seasonality and composition of dissolved nitrogen export from High Arctic headwater catchments
Bibliographic record
Abstract
This study investigates the impacts of active layer detachments (ALDs) on nitrogen in seasonal runoff from High Arctic hillslope catchments. We examined dissolved nitrogen in runoff from an undisturbed catchment (Goose (GS)) and one that was disturbed (Ptarmigan (PT)) by ALDs, prior to disturbance (2007) and 5 years after disturbance (2012). The seasonal dynamics of nitrogen species concentrations and fluxes were similar in both catchments in 2007, but the mean seasonal nitrate concentration and mass flux from the disturbed catchment were on the order of 30 times higher relative to the undisturbed catchment in 2012. Stormflow yielded 45% and 60% of the 2012 total dissolved nitrogen flux in GS and PT, respectively, although rainfall runoff provided less than 25% of seasonal discharge. Results support that through the combined effects of increased disturbance and rainfall, climate change stands to significantly enhance the export of nitrate from High Arctic watersheds. This study highlights that the increase in the delivery of nitrate from disturbance is especially pronounced late in the season when downstream productivity and the biological demand for this often limiting nutrient are high. Our results also demonstrate that the impact of ALDs on nitrate export can persist more than 5 years following disturbance.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".