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Geologic exploration activities increase methane emissions from boreal peatlands

2024· preprint· en· W4399726422 on OpenAlexaffabout
Percy Korsah, Scott J. Davidson, Maria Strack

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicPeatlands and Wetlands Ecology
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsPeatBorealBogEnvironmental scienceGreenhouse gasMireDisturbance (geology)Hydrology (agriculture)Atmospheric sciencesPhysical geographyEcologyGeologyGeographyOceanographyGeomorphology

Abstract

fetched live from OpenAlex

Boreal peatlands serve as long-term carbon (C) sinks as well as a significant source of methane (CH4) to the atmosphere. However, peatlands are threatened by both natural and anthropogenic disturbances resulting in potential release of large amounts of C to the atmosphere. Linear disturbances such as seismic lines for oil and gas exploration constitute the largest area of disturbance in boreal Canada. The impact of seismic lines on peatland function, such as C cycling and hydrology, is not very well understood, although physical changes in topography and lack of tree re-establishment are well documented. This study used the closed chamber technique to measure growing season understory CH4 fluxes on the footprint of the seismic line disturbance and in adjacent intact peatlands and assessed environmental controls on CH4 dynamics across a poor fen and two wooded bogs near Peace River, AB. Seismic lines were significantly warmer and wetter providing ideal conditions for increased CH4 emissions at all sites. Methane emissions relative to natural plots were almost tripled in the bogs (261- 308 %) and close to double in fens (176 %). The persistence of the seismic lines and the elevated CH4 emissions is a cause for concern due to CH4 having a higher global warming potential compared to CO2. Results from this study will contribute to accurate greenhouse gas (GHG) reporting for anthropogenic disturbances in boreal peatlands, currently lacking for many disturbance types, as well as provide a scientific foundation for integrated land management practices and policies related to peatland restoration.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.086
Threshold uncertainty score0.171

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.022
GPT teacher head0.258
Teacher spread0.237 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes2
Has abstractyes

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