Soil CO<sub>2</sub> and CH<sub>4</sub> effluxes in powerline rights-of-way and their adjacent forests
Bibliographic record
Abstract
Global decarbonization implies a large deployment of the power grid to convey electricity from production sites to users. The right-of-way (i.e. the cleared area below the pylons, where vegetation is periodically maintained) is a linear disturbance in the landscape that changes soil and vegetation and the carbon dynamics both within the impacted area as well as in adjacent forests, notably via an edge effect. Our main objective is to assess whether total and heterotrophic soil CO2 effluxes (FCO2-T and FCO2-H), soil CH4 effluxes (FCH4) and microclimate (soil temperature and water content)differed between powerline rights-of-way and their adjacent forests compared to forest interior over a large climatic gradient encompassing the temperate and boreal forest in Eastern Canada. Monthly efflux measurements were carried out between May and October 2023 and 2024 in rights-of-way, their adjacent forests and control forests in eight upland forest sites. Compared to the control forest, cumulative FCO2-T during the snow-free period were higher (+10.75 %) in the edge forests and lower (–7.75 %) in the rights-of-way, while cumulative FCO2-H were lower (–15.57 %) in the rights-of-way than in the control forests, with no edge effect. Soil was warmer and wetter in rights-of-way compared to control forest, but there was no edge effect on the microclimate. Land use change and edge effect did not alter the apparent temperature sensitivity of FCO2-T (Q10) or the CH4 absorption capacity of soils. Altogether, our results showed that the deployment of powerlines in a forest landscape creates spatial heterogeneity in soil CO2 effluxes with reduced CO2 cycling in the rights-of-ways and enhanced cycling in the edge forest, while soil CH4 effluxes are not affected. This suggests that the width of the right of way has implications for assessing the GHG impact of the deployment of power lines.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".