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Record W1902044804 · doi:10.1029/2004wr003099

Simulating soil water dynamics in a cutover bog

2004· article· en· W1902044804 on OpenAlexaff
Gavin Kennedy, Jonathan S. Price

Bibliographic record

VenueWater Resources Research · 2004
Typearticle
Languageen
FieldEnvironmental Science
TopicPeatlands and Wetlands Ecology
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsPeatWater tableHydrology (agriculture)Environmental scienceSoil scienceHydraulic conductivityWater contentBogInfiltration (HVAC)Water levelGeologySoil waterGroundwaterGeotechnical engineering

Abstract

fetched live from OpenAlex

A simulation model flow in cutover peat systems (FLOCOPS) was developed to improve the current understanding of the hydrology of cutover peatlands and the water management programs designed to restore them. FLOCOPS considers temporal variability in peat bulk density, shrinkage character and θ‐Ψ (soil moisture–pressure head) relationships, volume changes due to compression, and changes to saturated hydraulic conductivity ( K S ) and saturated volumetric soil moisture (θ S ). FLOCOPS was evaluated by comparing simulated and observed 1998–1999 trends in elevation change (thickness of peat deposit), water table, θ, and Ψ. FLOCOPS effectively represented observed trends in elevation change, θ, and Ψ. A sensitivity analysis indicated that FLOCOPS was most sensitive to the retention, storage, and consolidation characteristics of the peat. Volume changes reduced hydrological variability, whereas low water retentivity and high water storage helped maintain high and stable θ, Ψ, and water table position. The sensitivity of the peat system's hydrology to changes in pore structure suggests that minimizing changes to the peat's characteristic pore structure during extraction and subsequent abandonment of the peatland will result in significantly more favorable hydrological conditions for bog 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 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 categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.524
Threshold uncertainty score0.999

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)0.0020.002

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 head0.023
GPT teacher head0.293
Teacher spread0.270 · 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; both teacher heads agree on what is shown here.

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

Citations46
Published2004
Admission routes1
Has abstractyes

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