Examining the Ecosystem-Scale GHG Exchange Following the Acrotelm Harvesting Method (ACM) in Eastern Quebec
Bibliographic record
Abstract
Traditional horticultural peat production involves the removal of vegetation and drainage of peatlands, resulting in significant ecological disturbances and greenhouse gas (GHG) emissions. The Acrotelm Harvesting Method (ACM) was developed as a more sustainable alternative, designed to reduce these impacts by allowing peat extraction without large-scale ecosystem alterations. However, the ACM still necessitates the passage of machinery over the peat, potentially disturbing surface vegetation and altering the carbon dynamics. My project, based at a field site near Baie-Comeau, QC, represents the first comprehensive assessment of the ACM’s impact on net ecosystem exchanges of main GHGs. I focused on evaluating the ecosystem-scale effects by analyzing data collected from paired eddy covariance towers installed at both the control (unharvested) and harvested sites. These towers provide temporally continuous, spatially integrated flux measurements. Preliminary findings indicate that the site subjected to ACM exhibits altered surface topography, leading to overall wetter conditions, compaction of hummocks, and vegetation damage. These changes have resulted in reduced carbon dioxide uptake and increased methane emissions at the harvested site. Through the analysis and interpretation of this data, my research contributes to a better understanding of the ACM’s environmental impact and its potential as a sustainable peatland management strategy. This research was conducted under the supervision of Dr. Ian Strachan in the Atmospheric Environmental Research (AER) lab.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".