Overlooked sources of methane emissions from trees: branches and wounds
Bibliographic record
Abstract
Tree stems have been identified as globally significant methane (CH 4 ) sources; however, little information exists on emissions from tree wounds and branches. CH 4 emissions can occur from the decomposition of anaerobic heartwood, which is also associated with wounds; CH 4 may also be transported through the transpiration stream and emitted from branches. We compared CH 4 emissions between tree stems and branches and assessed whether trees with major wounds emit more than those without. CH 4 fluxes were measured from stems, branches, and wounds (classified as major or minor) of two dominant tree species in an upland temperate forest, and from the soil, and scaled up to the stand level. Branches and stems of both species emitted CH 4 , and the per unit area emission rates from branches were similar to (or in some cases greater than) stems. Trees with major wounds had greater CH 4 emission rates than those without, from unblemished sections of their stems and from the wounds. At the stand scale, branches, stems, and wounds accounted for 83%, 9%, and 8% of net CH 4 emissions from trees, respectively, and collectively offset 63% of the soil CH 4 sink. These results indicate that tree branches and wounds can be important CH 4 sources in forests.
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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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".