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Record W7132905535

Soil Methane Dynamics of Skid Trails and Landings in Managed Northern Hardwood Forests

2022· dissertation· W7132905535 on OpenAlexaboutno aff
Juliana Louise Vantellingen

Bibliographic record

VenueTSpace · 2022
Typedissertation
Language
FieldEngineering
TopicForest Biomass Utilization and Management
Canadian institutionsnot available
Fundersnot available
KeywordsSoil waterSkid (aerodynamics)Sink (geography)Hydrology (agriculture)Temperate climateSoil horizonSpatial variability
DOInot available

Abstract

fetched live from OpenAlex

Skid trails and landings are the portions of harvested forests that are used by forestry vehicles; skid trails are the network of trails used to transport logs to the landing, a central storage point where logs are processed and stacked. Soils in these features are highly impacted by the traffic they experience, and often sustain long-lasting changes to soil properties. While temperate forest soils tend to act as a methane (CH4) sink, these altered soil properties can influence soil CH4 dynamics, weakening their strength as a sink or causing a shift to CH4 emissions. This phenomenon has been documented in experimental settings but very few have studied it in situ in harvested forests as well as the soil drivers that contribute to it. We studied soil CH4 fluxes from skid trails and landings in a selection-managed forest in central Ontario, Canada, as well as the properties that may contribute to the observed flux. One-year-old skid trails exhibited high CH4 emissions, particularly from intensively used primary trails that were low-lying and wet. These emissions were correlated to low surface soil porosities and high soil moisture contents. Landings one year after harvest had significant spatial variation but had some strong “hotspots” of high CH4 emissions. CH4 flux on landings was correlated to soil pH and quantities of buried woody residues. On a broader scale, within the first year after harvest emissions from skid trails and landings respectively offset the strength of the CH4 sink from the surrounding untrafficked soils within a harvest area by ~45 and 12%. A chronosequence of skid trails and landings found that highest CH4 emissions occurred one year after a harvest event then began to decrease in the following years. Eventually soils returned to consuming CH4, however in the 15-year study period these soils never recovered to consuming CH4 at the same rate as unharvested soils. Temperate forest soils are an important global CH4 sink, and these findings contribute to the understanding of the effects of forest management on forest CH4 budgets. The results also inform prevention and remedial practices in climate smart forestry practices.

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 distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.225
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.009
GPT teacher head0.269
Teacher spread0.260 · 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 teacher head, not a consensus.

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
Published2022
Admission routes1
Has abstractyes

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