Inorganic nitrogen retention in acid‐sensitive lakes in southern Norway and southern Ontario, Canada—a comparison of mass balance data with an empirical N retention model
Bibliographic record
Abstract
Abstract In‐lake retention of inorganic nitrogen species (nitrate and ammonium) was estimated from mass balances in five acid‐sensitive lakes in southern Norway and eight in southern Ontario, Canada, to evaluate an empirical in‐lake N retention (RN) model. This model is included in the First‐order Acidity Balance (FAB) model, which currently is used for calculation of critical acid loads and exceedances in many countries. To estimate in‐lake RN, the FAB model uses a recommended mass transfer coefficient (SN) of 5 m year−1, which mainly is derived from NO3− mass balances in Canadian lakes. To date, the in‐lake RN model has not been evaluated for large parts of Europe. At the Norwegian study sites receiving the highest N deposition (>120 meq m−2 year−1) the net in‐lake retention of inorganic N (TIN) exceeded the corresponding terrestrial retention by a factor of 1·1–2·6. Despite differences in N loading and hydrology at the Norwegian and Canadian sites, both the mean mass transfer coefficients for NO3− (SNO3; 6·5 versus 5·6 m year−1) and TIN (STIN; 7·9 versus 7·0 m year−1) were of comparable magnitude. Both mean values and ranges of SNO3 suggest that the default SN value presently recommended for FAB model applications seems valid over a large range in N inputs and areal water loads (qs). However, owing to the relatively few data available for lakes with high qs values (15–150 m year−1), it is recommended that more lakes within this range be included in future studies to obtain a more precise prediction of in‐lake N retention over a wide qs gradient. Also, when considering that the FAB model treats all inorganic N leaching from a catchment as NO3−, it seems reasonable to use a default STIN value instead of just SNO3 when estimating in‐lake RN. In that case, the in‐lake RN presently calculated by the FAB model might be slightly underestimated. Copyright © 2003 John Wiley & Sons, Ltd.
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".