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Record W1489815246 · doi:10.1002/wrcr.20278

Peat properties and water retention in boreal forested peatlands subject to wildfire

2013· article· en· W1489815246 on OpenAlexaffabout
Dan K. Thompson, J. M. Waddington

Bibliographic record

VenueWater Resources Research · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicPeatlands and Wetlands Ecology
Canadian institutionsMcMaster UniversityNatural Resources CanadaCanadian Forest Service
Fundersnot available
KeywordsPeatEnvironmental scienceBorealSphagnumWater tableHydrology (agriculture)Water retentionMoistureClimate changeSoil scienceSoil waterGeologyGroundwaterEcologyChemistryOceanography

Abstract

fetched live from OpenAlex

[1] Peat cores from a recently burned peatland and one over 75 years since fire in Alberta, Canada were analyzed for physical properties and water retention. Wildfire exposed denser peat at the peat surface, more so in hollow than hummock microforms. Water retention in peat has implications for postfire Sphagnum regeneration, as this more dense peat requires smaller volumes of water loss before a critical growth-inhibiting pore-water pressure of −100 mb is reached. Simulations of water retention after fire showed that hollow microforms are at a higher risk of losing low-density surface peat, which moderates water table (WT) declines via high specific yield. Exposure of dense peat to the surface after fire increases surface moisture under a constant WT. The net effect of decreasing specific yield and increasing water retention at the surface has implications on hydrologic stability and resilience of boreal peatlands to future wildfire risk under a changing climate. Earth system models incorporating wildfire disturbance in boreal peatlands would benefit from the inclusion of these hydrological feedbacks in this globally significant carbon reservoir.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.059
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.040
GPT teacher head0.262
Teacher spread0.222 · 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

Citations65
Published2013
Admission routes2
Has abstractyes

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