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Record W2010602347 · doi:10.1139/t10-062

A new procedure for the determination of soil-water retention curves by continuous drying using high-suction tensiometers

2011· article· en· W2010602347 on OpenAlexvenueno aff
Sérgio D. N. Lourenço, Domenico Gallipoli, D. G. Toll, C.E. Augarde, F. D. Evans

Bibliographic record

VenueCanadian Geotechnical Journal · 2011
Typearticle
Languageen
FieldEngineering
TopicSoil and Unsaturated Flow
Canadian institutionsnot available
Fundersnot available
KeywordsSuctionSoil waterWettingGeotechnical engineeringSample (material)Water retentionWater retention curveWater contentStiffnessEnvironmental scienceMathematicsSoil scienceMaterials scienceGeologyChemistryComposite materialChromatographyEngineeringMechanical engineering

Abstract

fetched live from OpenAlex

Soil-water retention curves (SWRCs) can be determined using high-suction tensiometers (HSTs) following two different procedures that involve either continuous or discrete measurement of suction. In the former case, suction measurements are taken while the sample is permanently exposed to the atmosphere and the soil is continuously drying. In the latter case, the drying or wetting process is halted at different stages to ensure equalization within the sample before measuring suction. Continuous drying has the advantage of being faster; however, it has the disadvantage that the accuracy of mass measurements (necessary for the determination of water content) is affected by the weight and stiffness of the cable connecting the HST to the logger. To overcome this problem, an alternative continuous drying procedure is presented in this paper in which two separate but nominally identical samples are used to obtain a single SWRC; one sample is used for the mass measurements, while a second sample is used for suction measurements. It is demonstrated that the new continuous drying procedure gives SWRCs that are similar to those obtained by discrete drying.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.691
Threshold uncertainty score0.828

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 head0.021
GPT teacher head0.203
Teacher spread0.183 · 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

Citations46
Published2011
Admission routes1
Has abstractyes

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