Bibliographic record
Abstract
Peatland drainage and peat extraction changes natural peatlands from a net carbon sink to that of a large net source due to increased respiration and the removal of carbon dioxide (CO 2 ) fixing vegetation. Restoration of these altered peatland ecosystems is being applied to reduce these carbon emissions. As peatland restoration is a new and emerging land‐use management practice, the purpose of this research was to examine the impact of restoration on the methane (CH 4 ) component of the carbon cycle at the Bois‐des‐Bel peatland located near Rivière‐du‐Loup, Québec from early May to mid October for several years. The seasonal CH 4 fluxes prior to restoration at an extracted (cutover) and a restored peatland were not significantly different from each other or zero. However, three years postrestoration the seasonal CH 4 emissions at the restored site were 4.2 g m −2 CH 4 season −1 , 4.6 times greater than the cutover site. Ponds and ditches at the restored site were seasonal CH 4 emission hot spots (0.3 and 2.9 g m −2 CH 4 season −1 , respectively); however, emissions from herbaceous vegetation (1.0 g m −2 CH 4 season −1 ) were the dominant source of CH 4 from the restored peatland due to its large areal extent. CH 4 fluxes from the Bois‐des‐Bel peatland represented 14% of the total CO 2 ‐equivalent losses from the site. This study demonstrates the importance of vegetation succession on peatland‐atmosphere flux of CH 4 .
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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 | 0.000 | 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) | 0.001 | 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".