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Record W2109801746 · doi:10.1002/hyp.6379

Field methods for measuring hydraulic properties of peat deposits

2006· article· en· W2109801746 on OpenAlexafffundabout
J. M. Hogan, Garth van der Kamp, S. Lee Barbour, Roy H. Schmidt

Bibliographic record

VenueHydrological Processes · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicPeatlands and Wetlands Ecology
Canadian institutionsUniversity of Saskatchewan
FundersRoyal Geographical SocietyRoyal Canadian Geographical SocietyAssociation of Canadian Universities for Northern Studies
KeywordsPeatHydraulic conductivitySlug testGeologyDrainageBorealHydrology (agriculture)Water tableEnvironmental scienceGeotechnical engineeringSoil scienceSoil waterGroundwater

Abstract

fetched live from OpenAlex

Abstract New field techniques were developed and tested to evaluate peat storativity and hydraulic conductivity in a Boreal fen. Enclosed drainage tests and pumping tests were successfully completed in the thawed peat above an impermeable frozen layer and then repeated when the peat was fully thawed. A loading test experiment constrained values of vertical hydraulic conductivity within an order of magnitude for the peat below a depth of 2 m. An inherent advantage of these tests is that volumes of undisturbed peat on the scale of cubic metres may be characterized. Storativity of the fen peat as determined by enclosed drainage tests ranged from about 1·0 at the peat surface to 0·35 at a water table depth of 0·15 m. Laboratory drainage tests of peat cores gave similar, but widely scattered results. Hydraulic conductivity near the surface was as high as 9·0 × 10 −3 ms −1 determined with pumping tests and in the range of 10 −6 to 10 −5 ms −1 below a depth of 2 m, estimated with the loading test. Slug tests gave similar results. Pumping tests, enclosed storativity tests and loading tests are practical large‐scale field tests for determining peat properties. Copyright © 2006 Crown in the right of Canada, and John Wiley & Sons, Ltd.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.425
Threshold uncertainty score0.293

Codex and Gemma teacher scores by category

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 communication0.0000.000
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 head0.034
GPT teacher head0.268
Teacher spread0.233 · 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 designBench or experimental
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

Citations54
Published2006
Admission routes3
Has abstractyes

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