Heavy-Machinery Traffic Impacts Methane Emissions as Well as Methanogen Abundance and Community Structure in Oxic Forest Soils
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Temperate forest soils are usually efficient sinks for the greenhouse gas methane, at least in the absence of significant amounts of methanogens. We demonstrate here that trafficking with heavy harvesting machines caused a large reduction in CH(4) consumption and even turned well-aerated forest soils into net methane sources. In addition to studying methane fluxes, we investigated the responses of methanogens after trafficking in two different forest sites. Trafficking generated wheel tracks with different impact (low, moderate, severe, and unaffected). We found that machine passes decreased the soils' macropore space and lowered hydraulic conductivities in wheel tracks. Severely compacted soils yielded high methanogenic abundance, as demonstrated by quantitative PCR analyses of methyl coenzyme M reductase (mcrA) genes, whereas these sequences were undetectable in unaffected soils. Even after a year after traffic compression, methanogen abundance in compacted soils did not decline, indicating a stability of methanogens here over time. Compacted wheel tracks exhibited a relatively constant community structure, since we found several persisting mcrA sequence types continuously present at all sampling times. Phylogenetic analysis revealed a rather large methanogen diversity in the compacted soil, and most mcrA gene sequences were mostly similar to known sequences from wetlands. The majority of mcrA gene sequences belonged either to the order Methanosarcinales or Methanomicrobiales, whereas both sites were dominated by members of the families Methanomicrobiaceae Fencluster, with similar sequences obtained from peatland environments. The results show that compacting wet forest soils by heavy machinery causes increases in methane production and release.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it