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Record W2919968477 · doi:10.1002/hyp.13418

Possible soil tension controls on the isotopic equilibrium fractionation factor for evaporation from soil

2019· article· en· W2919968477 on OpenAlexaff
Marcel Gaj, Jeffrey J. McDonnell

Bibliographic record

VenueHydrological Processes · 2019
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGroundwater and Isotope Geochemistry
Canadian institutionsGlobal Institute for Water SecurityUniversity of Saskatchewan
Fundersnot available
KeywordsFractionationEquilibrium fractionationSoil waterIsotope fractionationSiltSoil textureSoil scienceGroundwaterWater contentEnvironmental scienceGeologyChemistryGeotechnical engineering

Abstract

fetched live from OpenAlex

Abstract The stable isotopes of hydrogen and oxygen (δ 2 H and δ 18 O) are useful conservative tracers for tracking the movement of water in soil. But although the tracking of water infiltrating through the soil profile and its movement as run‐off and groundwater recharge are well developed, water movement through the soil can also include evaporative fractionation. Soil water fractionation factors have, until now, been largely empirical. Unlike open water evaporation where temperature, humidity, and vapour pressure gradient define fractionation, soil water evaporation includes fractionation by soil matrix effects. These effects are still poorly characterized. Here, we present preliminary results from a simple laboratory experiment with four soil admixtures with grain sizes that range from sand to silt and clay. Our results show that soil tension seems to control the isotope fractionation of resident soil water. The relationship between soil tension and equilibrium fractionation appears to be independent of soil texture and appears well supported by thermodynamic theory. Although these results are preliminary, they suggest that future work should go after soil tension effects as a possible explanatory factor of soil water and water vapour fractionation.

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 categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.234
Threshold uncertainty score0.997

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)0.0030.000

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 head0.025
GPT teacher head0.222
Teacher spread0.197 · 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.

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

Citations51
Published2019
Admission routes1
Has abstractyes

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