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Record W4214932436 · doi:10.1139/cgj-2020-0086

Consequences of drying on the hydro-mechanical response of fibrous peats upon compression

2022· article· en· W4214932436 on OpenAlexvenueno aff
Hongfen Zhao, Cristina Jommi

Bibliographic record

VenueCanadian Geotechnical Journal · 2022
Typearticle
Languageen
FieldEngineering
TopicGeotechnical Engineering and Soil Stabilization
Canadian institutionsnot available
FundersNatural Science Foundation of Guangdong ProvinceNational Natural Science Foundation of ChinaEuropean Commission
KeywordsPeatVoid ratioWater contentCompressibilityMacroporeGeotechnical engineeringHydraulic conductivityOedometer testGeologySoil scienceEnvironmental scienceChemistrySoil waterEcology

Abstract

fetched live from OpenAlex

Peats are encountered in waterlogged deltaic areas, where degradation is delayed by favourable environmental conditions. The recent increase in frequency and severity of droughts is expected to accelerate peat degradation, in turn increasing subsidence and flood risk, urging better understanding of the response of peats to drying events. To this aim, compression tests on natural and reconstituted peat samples were performed, supported by X-ray micro-computed tomography. The peat fabric was found to be the key factor in the response to drying, with fibres playing the most significant role. Drying in peats starts affecting the macro-fabric, with an irreversible reduction in volume and disruption of the fibrous network occurring under saturated conditions until a threshold void ratio is reached, below which desaturation occurs of the intra-fibres and intra-peds pores. The first drying stage dramatically decreases the compressibility, while the hydraulic conductivity is hardly affected due to the enlargement of macropores. Secondary compressibility is affected by the peat fabric besides the organic content. The total organic content does not change substantially during drying; hence, it is not the best proxy to describe the consequences of drying on the response of fibrous peats. The fibre content can be better used to serve the aim.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.041
Threshold uncertainty score0.440

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 integrity0.0000.001
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 head0.014
GPT teacher head0.204
Teacher spread0.191 · 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 designSimulation or modeling
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

Citations6
Published2022
Admission routes1
Has abstractyes

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