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 ( R N ) 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 R N , the FAB model uses a recommended mass transfer coefficient ( S N ) of 5 m year −1 , which mainly is derived from NO 3 − mass balances in Canadian lakes. To date, the in‐lake R N 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 NO 3 − ( S NO3 ; 6·5 versus 5·6 m year −1 ) and TIN ( S TIN ; 7·9 versus 7·0 m year −1 ) were of comparable magnitude. Both mean values and ranges of S NO3 suggest that the default S N value presently recommended for FAB model applications seems valid over a large range in N inputs and areal water loads ( q s ). However, owing to the relatively few data available for lakes with high q s 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 q s gradient. Also, when considering that the FAB model treats all inorganic N leaching from a catchment as NO 3 − , it seems reasonable to use a default S TIN value instead of just S NO3 when estimating in‐lake R N . In that case, the in‐lake R N presently calculated by the FAB model might be slightly underestimated. Copyright © 2003 John Wiley & Sons, Ltd.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.000 | 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 teacher head, 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".