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Record W1497087233

Wildfire Impacts on Peatland Ecohydrology

2012· dissertation· en· W1497087233 on OpenAlexfundaboutno aff
Dan K. Thompson

Bibliographic record

VenueMacSphere (McMaster University) · 2012
Typedissertation
Languageen
FieldEnvironmental Science
TopicPeatlands and Wetlands Ecology
Canadian institutionsnot available
FundersCanadian Forest ServiceNatural Sciences and Engineering Research Council of CanadaU.S. Forest Service
KeywordsEcohydrologyPeatEnvironmental scienceGeographyHydrology (agriculture)Physical geographyEcologyGeologyEcosystemBiologyArchaeologyGeotechnical engineering
DOInot available

Abstract

fetched live from OpenAlex

Forested peatlands of the Canadian boreal forest represent a globally significant carbon pool subject to periodic disturbance from wildfire. The current ecohydrological conceptualization of peatland recovery from (and resilience to) disturbance is largely based around work on cutover peatlands. Wildfire disturbance is simultaneously more spatially extensive and more variable in the severity of disturbance compared to cutover peatlands. The objective of this thesis is to examine the changes to peatland ecohydrological processes as a result of wildfire disturbance in forested ombrotrophic peatlands of the Boreal Plains. The hydrology and atmospheric exchanges of energy and water were examined at two peatlands in northern Alberta: one recently burned and the other approximately 75 years since fire. Wildfire resulted in little change in net radiation flux to the peatland during the snow-free period. A decrease in the net radiation flux during the late winter was caused by the loss of the tree canopy and the increase in albedo during winter. While summer albedo largely returned to pre-fire values within two years after fire, the amount of solar

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 score0.504
Threshold uncertainty score0.998

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.008
GPT teacher head0.197
Teacher spread0.190 · 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

Citations11
Published2012
Admission routes2
Has abstractyes

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