Progress in Nitrogen Deposition Monitoring and Modelling
Bibliographic record
Abstract
The chapter reviews progress in monitoring and modelling of atmospheric nitrogen (N) deposition at regional and global scales. The Working Group expressed confidence in the inorganic N wet deposition estimates in U.S., eastern Canada, Europe and parts of East Asia. But, long-term wet or dry N deposition information in large parts of Asia, South America, parts of Africa, Australia/Oceania, and oceans and coastal areas is lacking. Presently, robust estimates are only available for inorganic N as existing monitoring generally does not measure the complete suite of N species, impeding the closing of the atmospheric N budget. The most important species not routinely measured are nitrogen dioxide (NO2), ammonia (NH3), organic N and nitric acid (HNO3). Uncertainty is much higher in dry deposition than in wet deposition estimates. Inferential modelling (combining air concentrations with exchange rates) and direct flux measurements are good tools to estimate dry deposition; however, they are not widely applied. There is a lack of appropriate parameterizations for different land uses and compounds for input into inferential models. There is also a lack of direct dry deposition flux measurements to test inferential models and atmospheric model estimates. Quelle: http://link.springer.com
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.005 |
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".