Landfill cover soils: variable moisture and temperature effects on methane oxidation
Bibliographic record
Abstract
Landfills are one of the largest anthropogenic sources of methane (CH4), comprising over 20% of \nCanada’s CH4 emissions. Hot-spots of CH4 emissions in landfill cover soils have shown an enrichment of \nmicrobes that consume CH4 and produce carbon dioxide (CO2) through CH4 oxidation, which can act as \na natural solution to reduce CH4 emissions. CH4 oxidation is affected by soil moisture and temperature, \nalthough their simultaneous effects on CH4 oxidation rates have not been well-studied. Here, we \nconducted a closed-headspace batch experiment with cover soil from a former landfill in Waterloo, \nOntario, to measure CH4 oxidation and CO2 efflux rates associated with variations in soil moisture and \ntemperature simultaneously. The soil samples were prepared under 5 soil moisture contents (% WFPS; \nwater-filled pore space), ranging from 11 to 47% WFPS, and incubated following a regime whereby \ntemperatures increased from 1 to 35°C (Phase I) then decreased from 35 to 1°C (Phase II). Every 2 days, \nthe temperature was adjusted to the next value for a 24-hour acclimation period while open to the \natmosphere, then the headspace was closed and spiked with CH4 (150 ppm). Headspace CH4 and CO2 \nconcentrations were measured over 2 hours to calculate apparent CH4 oxidation and CO2 efflux rates. \nThe maximum CO2 efflux rate was observed at the maximal WFPS and temperature conditions of this \nexperiment (91.5 nmol h-1 g dry wt.-1 at 47% WFPS and 35°C). In contrast, the maximum CH4 oxidation \nrates were observed at intermediate WFPS and temperature conditions (1.86 nmol h-1 g dry wt.-1 at \n25% WFPS and 25°C). These experimental results provide insight into favourable WFPS and temperature \nconditions for CH4 oxidation, and therefore into how seasonal changes in WFPS and temperature could \nimpact CH4 oxidation.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".