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Record W2109908158 · doi:10.2136/sssaj2007.0111n

A Method to Determine Unsaturated Hydraulic Conductivity in Living and Undecomposed <i>Sphagnum</i> Moss

2008· article· en· W2109908158 on OpenAlexaff
Jonathan S. Price, Pete Whittington, D. E. Elrick, Maria Strack, N. Brunet, Erica Faux

Bibliographic record

VenueSoil Science Society of America Journal · 2008
Typearticle
Languageen
FieldEnvironmental Science
TopicPeatlands and Wetlands Ecology
Canadian institutionsUniversity of GuelphUniversity of Waterloo
Fundersnot available
KeywordsSphagnumHydraulic conductivityMossWater retentionPeatSoil scienceSoil waterWater contentEnvironmental scienceVadose zoneWater transportWater flowMoistureSaturation (graph theory)Hydrology (agriculture)ChemistryGeologyBotanyEcologyGeotechnical engineering

Abstract

fetched live from OpenAlex

Sphagnum mosses ( Sphagnum L.) are the primary peat‐forming plant in northern peatlands and rely on capillary transport of water to facilitate physiological processes. The unsaturated hydraulic conductivity of the living, undecomposed, and poorly decomposed mosses is needed to estimate and model water flux to their growing upper layer. This study describes a new apparatus to measure this in the highly porous (∼90%) hummock profile where the pore sizes are large and the mosses delicate, in which established methods do not work. Independent tension disks controlled the pressure head (ψ, between 0 and −35 cm of water) and the pressure gradient and thus flow. The uppermost 5‐cm layer of moss had a saturated hydraulic conductivity of 1800 μm s −1 , and decreased when unsaturated (ψ = −25 cm of water) to 0.03 μm s −1 Moss 25 cm below the surface had equivalent values of 230 and 11.0 μm s −1 at moisture contents of 0.18 to 0.22 m 3 m −3 The The soil water retention model RETC provided a good fit for both hydraulic conductivity and water retention when fitted simultaneously, but did not perform well to predict hydraulic conductivity from water retention data alone.

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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.407
Threshold uncertainty score0.583

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.002
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 head0.018
GPT teacher head0.269
Teacher spread0.251 · 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

Citations105
Published2008
Admission routes1
Has abstractyes

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