Nitrous Oxide Fluxes in Three Experimental Boreal Forest Reservoirs
Bibliographic record
Abstract
Global atmospheric concentrations of nitrous oxide (N2O), a powerful greenhouse gas, continue to increase. While many sources and sinks have been identified, there is little known about how existing and newly constructed reservoirs, such as those created for hydroelectric production, impact current atmospheric N2O concentrations. We hypothesized that N2O fluxes to the atmosphere would increase because enhanced nutrient availability and increased soil respiration following the flooding of soils during reservoir creation would favor denitrification. Furthermore, we hypothesized that emissions would be linked to the amount of organic carbon contained in the flooded landscape. These hypotheses were tested by creating three experimental reservoirs over boreal upland subcatchments that ranged in the amount of organic carbon stored in soils and vegetation. Diffusive surface N2O fluxes within each reservoir were estimated using surface water concentrations of N2O and the thin boundary layer method. Surface fluxes ranged from -1.0 to -3.5 microg N2O m(-2) d(-1), and water column N2O concentrations indicated that contrary to expectations, the reservoirs were acting as slight sinks for atmospheric N2O. This net consumption of N2O was likely related to an excess of labile carbon and low concentrations of oxygen (O2) and nitrate (NO3-) in the flooded soils. Therefore, it is postulated that reservoir creation by flooding boreal soils will likely have little or no net effect of adding additional N2O to the current greenhouse gas (GHG) atmospheric burden, at least over the short term.
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 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 | 0.000 | 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 | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 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".