Land use history determines non-native earthworm impacts on atmospheric methane consumption in forest soils, central New York State
Bibliographic record
Abstract
Yavitt, J. B. 2015. Land use history determines non-native earthworm impacts on atmospheric methane consumption in forest soils, central New York State. Can. J. Soil Sci. 95: 321–330. I used complementary field and laboratory studies to examine the impact of two types of soil disturbance on net consumption of atmospheric methane (CH4) in forest soils near Ithaca, New York. One type of disturbance is invasion of non-native earthworms that mix soil layers, and the other is agriculture, which reduces the pit-and-mound surface topography to a flat landscape. Measurements of CH4 fluxes between soil and the atmosphere were made in autumn before seasonal leaf fall when earthworms had consumed all of the previous year's leaf fall, and revealed no impact by earthworms in the never-tilled soils. Although earthworms did lead to greater consumption of atmospheric CH4 in the post-agriculture soils, the mean consumption rate was only −0.2 mg m2 d−1. Concentrations of atmospheric CH4 in wormed soils were often greater than that in the atmosphere, suggesting that earthworms promote CH4 production. In general, earthworms decreased soil permeability to gas diffusion. Post-agriculture soils also had faster CH4 oxidation rates when incubated in the laboratory. The results taken together suggest that earthworm impacts on atmospheric CH4 consumption depend on the history of soil disturbance.
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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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".