Near-zero methane emission from an abandoned boreal peatland pasture based on eddy covariance measurements
Bibliographic record
Abstract
Although estimates of the annual methane (CH4) flux from agriculturally managed peatlands exist, knowledge of controls over the variation of CH4 at different time-scales is limited due to the lack of high temporal-resolution data. Here we present CH4 fluxes measured from May 2014 to April 2016 using the eddy covariance technique at an abandoned peatland pasture in western Newfoundland, Canada. The goals of the study were to identify the controls on the seasonal variations in CH4 flux and to quantify the annual CH4 flux. The seasonal variation in daily CH4 flux was not strong in the two study years, however a few periods of pronounced emissions occurred in the late growing season. The daily average CH4 flux was small relative to other studies, ranging from -4.1 to 9.9 nmol m-2 s-1 in 2014-15 and from -7.1 to 12.1 nmol m-2 s-1 in 2015-16. Stepwise multiple regression was used to investigate controls on CH4 flux and this analysis found shifting controls on CH4 flux at different periods of the growing season. During the early growing season CH4 flux was closely related to carbon dioxide fixation rates, suggesting substrate availability was the main control. The peak growing season CH4 flux was principally controlled by the CH4 oxidation in 2014, where the CH4 flux decreased and increased with soil temperature at 50 cm and soil water content at 10 cm, but a contrasting temperature-CH4 relation was found in 2015. The late growing season CH4 flux was found to be regulated by the variation in water table level and air temperature in 2014. The annual CH4 emission was near zero in both study years (0.36 ± 0.30 g CH4 m-2 yr-1 in 2014-15 and 0.13 ± 0.38 g CH4 m-2 yr-1 in 2015-16), but fell within the range of CH4 emissions reported for agriculturally managed peatlands elsewhere.
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.
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) | 0.000 | 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 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".