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The effect of soil compaction on the water retention characteristics of soils in forest plantations

2001· article· en· W2000665920 on OpenAlexaff
C. W. Smith, M. A. Johnston, Simon Lorentz

Bibliographic record

VenueSouth African Journal of Plant and Soil · 2001
Typearticle
Languageen
FieldEngineering
TopicSoil and Unsaturated Flow
Canadian institutionsNutrasource
Fundersnot available
KeywordsCompactionSoil waterWater contentSoil compactionWater retention curveWater retentionSoil scienceWater potentialBulk densityField capacityEnvironmental scienceAvailable water capacityGeotechnical engineeringGeology

Abstract

fetched live from OpenAlex

A study was carried out to evaluate the relative effects of soil compaction on the water retention characteristics of a range of soils in forest plantations in the summer rainfall regions of South Africa. In all cases compaction resulted in the ‘flattening’ of the S-shaped water retentivity curve expressed on either a mass or volumetric basis. This had the implicit effect of lowering the water release index (a log-linear plot of matric potential against water content). A clear relationship between available water capacity (AWC) and bulk density and soil type was not discernible since changes in water retentivity curves following compaction are dependent upon the complex relationship between compressive processes, soil properties and pore geometry. AWC responded to compaction in three ways: (i) AWC was reduced with increasing compaction (most soils); (ii) increasing compaction resulted in increasing AWC (some sandy and clayey soils) and (iii) increasing compaction resulted in increasing AWC up to a point after which it declined. In general, field capacity (FC) increased with increasing compaction when water content was expressed on a volumetric basis but no clear trends were apparent on a mass basis. Effects of compaction on AWC depend upon the designated matric potential for FC which is often arbitrarily defined since it may vary for different soils and crops. Changes in pore geometry are better reflected by the expression of the water content on a mass basis without consideration of volume effects. From a practical point of view, however, changes in available water are better expressed on a volumetric basis.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.078
Threshold uncertainty score0.142

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 head0.010
GPT teacher head0.187
Teacher spread0.177 · 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 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

Citations53
Published2001
Admission routes1
Has abstractyes

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