Log landings are methane emission hotspots in managed forests
Bibliographic record
Abstract
Log landings are areas within managed forests used to process and store felled trees prior to transport. Through their construction and use soil is removed or redistributed, compacted, and organic matter contents may be increased by incorporation of wood fragments. The effects of these changes to soil properties on methane (CH4) flux are unclear and unstudied. We quantified CH4 flux rates from year-old landings in Ontario, Canada, and examined spatial variability and relationships to soil properties within these sites. Landings emitted CH4 throughout the growing season; the average CH4 emission rate from log landings was 69.2 ± 12.8 nmol·m−2·s−1 (26.2 ± 4.8 g CH4-C·m−2·year−1), a rate comparable to CH4-emitting wetlands. Emission rates were correlated to soil pH, organic matter content, and quantities of buried woody debris. These properties led to strong CH4 emissions, or “hotspots”, in certain areas of landings, particularly where processing of logs occurred and incorporated woody debris into the soil. At the forest level, emissions from landings were estimated to offset ∼12% of CH4 consumption from soils within the harvest area, although making up only ∼0.5% of the harvest area. Management practices to avoid or remediate these emissions should be developed as a priority measure in “climate-smart” forestry.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".