Nitrous Oxide and Dinitrogen: The Missing Flux in Nitrogen Budgets of Forested Catchments?
Bibliographic record
Abstract
Most forest nitrogen budgets are imbalanced, with nitrogen inputs exceeding nitrogen outputs. The denitrification products nitrous oxide (N 2 O) and dinitrogen (N 2 ) represent often-unmeasured fluxes that may close the gap between explained nitrogen inputs and outputs. Gaseous N 2 O and N 2 effluxes, dissolved N 2 O flux, and traditionally measured dissolved nitrogen species (i.e., nitrate, ammonium, and dissolved organic nitrogen) were estimated to account for the annual nitrogen output along hillslope gradients from two catchments in a temperate forest. Adding N 2 O and N 2 effluxes to catchment nitrogen output not only reduced the discrepancy between nitrogen inputs and outputs (9.9 kg ha –1 yr –1 and 6.5 or 6.3 kg ha –1 yr –1, respectively), but also between nitrogen outputs from two catchments with different topographies (6.5 kg ha –1 yr –1 for the catchment with a large wetland, 6.3 kg ha –1 yr –1 for the catchment with a very small wetland). Dissolved N 2 O comprised a very small portion of the annual nitrogen outputs. Nitrogen inputs exceeded nitrogen outputs throughout the year except during spring runoff, and also during autumn storms in the catchment with the large wetland. Failing to account for denitrification products, especially during summer rainfall events, may lead to underestimation of annual nitrogen losses.
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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 | 0.002 |
| 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 | 0.002 |
| 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".