Potential next term fluxes of N2O and CH4 from soils of three forest types in Eastern Canada.
Bibliographic record
Abstract
We conducted laboratory incubation experiments to elucidate the influence of forest type and topographic position on emission and/or consumption previous termpotentialsnext term of nitrous oxide (N2O) and methane (CH4) from soils of three forest types in Eastern Canada. Soil samples collected from deciduous, black spruce and white pine forests were incubated under a control, an NH4NO3 amendment and an elevated headspace CH4 concentration at 70% water-filled pore space (WFPS), except the poorly drained wetland soils which were incubated at 100% WFPS. Deciduous and boreal forest soils exhibited greater previous termpotentialnext term of N2O and CH4 fluxes than did white pine forest soils. Mineral N addition resulted in significant increases in N2O emissions from wetland forest soils compared to the unamended soils, whereas well-drained soils exhibited no significant increase in N2O emissions in-response to mineral N additions. Soils in deciduous, boreal and white pine forests consumed CH4 when incubated under an elevated headspace CH4 concentration, except the poorly drained soils in the deciduous forest, which emitted CH4. CH4 consumption rates in deciduous and boreal forest soils were twice the amount consumed by the white pine forest soils. The results suggest that an episodic increase in reactive N input in these forests is not likely to increase N2O emissions, except from the poorly drained wetland soils; however, long-term in situ N fertilization studies are required to validate the observed results. Moreover, wetland soils in the deciduous forest are net sources of CH4 unlike the well-drained soils, which are net sinks of atmospheric CH4. Because wetland soils can produce a substantial amount of CH4 and N2O, the contribution of these wetlands to the total trace gas fluxes need to be accounted for when modeling fluxes from forest soils in Eastern Canada.
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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 | 0.000 | 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 | 0.000 |
| Scholarly communication | 0.001 | 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 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".