Effect of mounding, drainage and fertilization on CH <sub>4</sub> fluxes and methane-cycling functional genes in waterlogged forest stands
Bibliographic record
Abstract
Abstract Site preparation techniques including mounding, drainage and nitrogen (N) fertilization can enhance seedling survival and site productivity, particularly in waterlogged, low-productivity forest stands. However, practices that alter soil conditions and site hydrology can lead to the unintended alteration of biogeochemical process rates, such as CH 4 fluxes. This study sought to measure CH 4 fluxes measured using static closed chambers at a sub-boreal spruce site and a coastal cedar-hemlock site that underwent mounding and drainage, respectively, to manipulate water table depth relative to planted seedlings, as well as fertilization. The abundance of methyl coenzyme M reductase ( mcrA ) gene found in methanogenic archaea and the particulate methane monooxygenase ( pmoA ) gene found in methane-oxidizing bacteria (MOB) were examined. The use of sulphate as a potential method to stimulate sulphate-reducing bacteria (SRB) and reduce methanogen activity was also investigated using the dissimilatory sulfite reductase β-subunit ( dsrB ) gene. qPCR was used to link mcrA,pmoA and dsrB gene abundance to soil factors and GHG fluxes. Mounding created hot-spots of CH 4 emissions at the spruce site. Drainage improved soil aeration at the coastal cedar-hemlock site and reduced CH 4 emission rates. Fertilization did not affect CH 4 emissions from either site. CH 4 rates were influenced by soil water content and mcrA abundance. Measurements of microbial functional genes can elucidate the effects of site preparation on GHG fluxes in waterlogged forest stands.
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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 | 0.000 | 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".