Modelling the effects of changing climate and nitrogen deposition on nitrate dynamics in a Scottish mountain catchment
Bibliographic record
Abstract
The effect of changing climate and N deposition on montane ecosystems is a topic of considerable importance. Mountains are vulnerable environments and their ecosystems are often in a delicate balance. An application of the INCA-N model is presented to simulate current-day nitrate dynamics in a Scottish mountain lake and to project the possible future effects of climate change and reductions in N deposition on lake nitrate concentration ([NO3−]). The INCA-N model is calibrated using data from 1996–2006 in an attempt to determine the controls on [NO3−] in Lochnagar and process sensitivities to changing climate. Predictions were sensitive to hydrologic, vegetation-related and in-soil processes. Over the longer term, surface water [NO3−] in this mountain ecosystem is expected to increase. From 2020 to 2100, when N deposition is modelled at a constant rate, warmer temperature exerts a stronger effect on N losses to the lake surface than the N deposition. While the effects of a warming climate are projected to lead to increased surface water [NO3−], concentrations are not projected to either return to, or exceed, historical levels.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".