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Record W2895149441 · doi:10.1139/cgj-2018-0402

Modelling effects of root growth and decay on soil water retention and permeability

2018· article· en· W2895149441 on OpenAlexvenueno aff
Junjun Ni, Anthony Kwan Leung, Charles Wang Wai Ng

Bibliographic record

VenueCanadian Geotechnical Journal · 2018
Typearticle
Languageen
FieldEngineering
TopicSoil and Unsaturated Flow
Canadian institutionsnot available
FundersNational Key Research and Development Program of ChinaHong Kong University of Science and TechnologyMinistry of Science and Technology of the People's Republic of ChinaNational Natural Science Foundation of China
KeywordsSoil waterVoid ratioWater retentionPermeability (electromagnetism)Soil scienceSink (geography)Water retention curveGeotechnical engineeringShrinkageEnvironmental scienceMathematicsGeologyChemistryStatistics

Abstract

fetched live from OpenAlex

Plant roots can change the soil water retention curve (SWRC) and saturated permeability (k sat ) of vegetated soils. However, there is no model that could capture both the effects of root growth and root decay on these soil hydraulic properties simultaneously. This note proposes a new void ratio function that can model the decrease and increase in soil void ratio due to root occupancy (upon growth) and root shrinkage (upon decay), respectively, in an unsaturated vegetated coarse-grained soil. The function requires two root parameters; namely, root volume ratio and root decay ratio, both of which can be readily measured through root excavation and image-based analysis. The new function is incorporated into a void ratio–dependent SWRC model for predicting the SWRC of vegetated soils. Similarly, the same function can be combined with the Kozeny–Carman equation for predicting k sat . The model prediction is then compared with a set of new field test data and an existing laboratory dataset for a silty sand vegetated with plant species under the family Schefflera. Good agreements are obtained between the measurements and predictions.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.260
Threshold uncertainty score0.298

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 head0.008
GPT teacher head0.181
Teacher spread0.173 · 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

Citations123
Published2018
Admission routes1
Has abstractyes

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