How mounds are made matters: seismic line restoration techniques affect peat physical and chemical properties throughout the peat profile
Bibliographic record
Abstract
Seismic lines are prominent linear disturbances across boreal Canada with large-scale consequences to wildlife and ecosystem function. Although seismic line restoration has been observed to improve tree growth and survival, application in peatlands has been shown to alter ecosystem functions such as hydrology and carbon storage. The most common active restoration method is called mechanical mounding, where the classic technique inverts the peat profile. New mounding methods that maintain the peat profile may provide benefits by preserving existing vegetation and reducing disturbance. To determine the effects of different mounding methods on soil quality, we collected and analyzed peat cores from two different sites for various soil properties (C/N ratios, δ13C, δ15N, and Fourier transform infrared (FTIR) spectroscopy humification indices). Vegetation surveys were also conducted. The two sites are both a collection of seismic lines crossing poor fens in Alberta. One site was treated with the classic method, while the other was treated with two new mounding methods. Classic mechanical mounding significantly increased the degree of decomposition, indicative of lower substrate quality. Mechanical mounding also greatly reduced moss cover and introduced large amounts of bare ground cover. The two newer mounding methods did not result in these changes and were largely comparable to natural peat properties and vegetation communities. Preserving the peat profile in new mounding methods may support faster return of ecosystem function.
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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 | 0.001 | 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".