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Record W2135285641 · doi:10.1029/2004wr003732

Dynamics of biogenic gas bubbles in peat: Potential effects on water storage and peat deformation

2005· article· en· W2135285641 on OpenAlexafffundabout
Erik Kellner, J. M. Waddington, Jonathan S. Price

Bibliographic record

VenueWater Resources Research · 2005
Typearticle
Languageen
FieldEnvironmental Science
TopicPeatlands and Wetlands Ecology
Canadian institutionsUniversity of WaterlooMcMaster University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPeatWater tablePiezometerBuoyancySoil waterHydrology (agriculture)Environmental scienceDiel vertical migrationGeologyVolume (thermodynamics)Pore water pressureSoil scienceWater contentGroundwaterAquiferGeotechnical engineeringMechanicsEcology

Abstract

fetched live from OpenAlex

Dynamics of biogenic bubbles in peat soils were studied at a field site in southern Québec, Canada. The maximum gas content measured in this study varied spatially with a maximum seasonal increase in volumetric gas content of 0.15. The size of changes in total gas content of a 1 m deep profile was comparable to the seasonal water storage change. Changes in bubble volume in the saturated zone alter the water table level and, consequently, the water content in the unsaturated zone and the apparent water budget. In highly compressible soils (and floating root mats), buoyancy forces from bubbles also cause relations between the surface and the water table to change. These effects cannot be omitted in modeling the hydrology of peatlands. Our results indicate a great spatial variability of trapped bubbles. Using pressure transducers sealed to the surface, we found pressure deviations indicating small areas closed off by bubbles clogging the pores. The hydrological influence of these areas may be considerable as they may restrict or deflect water flows. Open pipe piezometers did not show these pressure deviations, possibly because the closed zones were too small to influence the head in pipes or because of less amount of gas close to the pipe screen.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.098
Threshold uncertainty score0.396

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 science0.0000.000
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 head0.010
GPT teacher head0.250
Teacher spread0.240 · 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.

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

Citations95
Published2005
Admission routes3
Has abstractyes

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