CO2 uptake decreased and CH4 emissions increased in first two years of peatland seismic line restoration
Bibliographic record
Abstract
Abstract Oil and gas exploration has resulted in over 300,000 km of linear disturbances known as seismic lines, throughout boreal peatlands across Canada. Sites are left with altered hydrologic and topographic conditions that prevent tree re-establishment. Restoration efforts have concentrated on tree recovery through mechanical mounding to re-create microtopography and support planted tree seedlings to block sightlines and deter predator use, but little is known about the impact of seismic line disturbance or restoration on peatland carbon cycling. This study looked at two mounding treatments and compared carbon dioxide and methane fluxes to untreated lines and natural reference areas in the first two years post-restoration. We found no significant differences in net ecosystem CO2 exchange, but untreated seismic lines were slightly more productive than natural reference areas and mounding treatments. Both restoration treatments increased ecosystem respiration, decreased net productivity by 6–21 gCO2m− 2d− 1, and created areas of increased methane emissions, including an increase in the contribution of ebullition, of up to 2000 mgCH4m− 2d− 1. Further research on this site to assess the longer-term impacts of restoration, as well as application on other sites with varied conditions, will help determine if these restoration practices are effective.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".