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Record W3046263574

Dissolved organic matter in the unsaturated zone: the view from the cave

2017· article· en· W3046263574 on OpenAlexaff
Andy Baker, Wuhui Duan, Helen Rutlidge, Liza K. McDonough, Phetdala Oudone, Karina Meredith, Martin S. Andersen, Denis M. O’Carroll, Katie Coleborn, Pauline C. Treble

Bibliographic record

VenueUNSWorks (University of New South Wales, Sydney, Australia) · 2017
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicKarst Systems and Hydrogeology
Canadian institutionsWestern University
Fundersnot available
KeywordsCaveOrganic matterGeologyDissolved organic carbonVadose zoneEnvironmental scienceHydrology (agriculture)Physical geographyOceanographyGeographyGroundwaterEcologyArchaeologyBiologyGeotechnical engineering
DOInot available

Abstract

fetched live from OpenAlex

Soil organic matter content is typically a few percent of the total soil composition. Diffuse recharge can mobilise some of this soil-derived organic matter. While soil pore water dissolved organic matter (DOM) concentrations are up to 100 ppm, the resulting groundwater dissolved organic matter concentration is typically less than 2ppm. Dissolved organic matter transported from the soil can be both biodegraded and sorbed to minerals, and the relative importance of these two processes in the unsaturated zone is poorly understood. Caves in karstified limestone uniquely provide direct access to water percolating from the soil to the groundwater. Cave percolation waters can be analysed for their DOM concentration and character. This provides insights into the extent and type of biological and chemical processing of DOM during transport from the soil to the groundwater. We determine the concentration and characteristics of DOM in cave percolation waters using liquid chromatography (LC-OCD) and optical spectrophotometry (fluorescence and absorbance). We sample DOM from multiple caves in SE Australia (Cathedral Cave, Wellington; South Glory and Harrie Wood Caves, Yarrangobilly), permitting comparison of unsaturated zone DOM properties at different depths (up to 30m below land surface) and different climate zones (montane and temperate). We use caves with long-term hydrological monitoring programs so that DOM in waters of contrasting residence times can be compared. Additionally, we compare these cave percolation water DOM characteristics to those from local and regional groundwater, sampled from nearby wells. Our results will help improve our understanding of how DOM is processed from soil to groundwater, and is also relevant to speleothem scientists interested in using organic matter preserved in speleothems as a paleoclimate or paleoenvironmental proxy. Plain Language Summary When plants die, they break down to organic matter, which forms part of the soil. When this organic matter is washed out of the soil and into the subsurface, we know very little about what happens next. Partly it is because we can't see and measure what is happening. There is a solution. We can use caves as observatories. We can collect the organic matter in the water which enters the caves, and analyse it back in the laboratory. There, we can determine not only the cocntration of organic matter, but also its chemical composition. Why is this important? There's lot of organic matter in soil. But only one or two organic molecues per million water molecules are present in groundwater. Where does it all go? One idea is that it is used as food by subterranean microbes. Another is that is sorbed to minerals. By measuring the chemical composition of organic matter in cave drip waters, we can work out which is more important, and help understand why there is so little organic matter in groundwater.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.028
Threshold uncertainty score0.055

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0010.002
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.043
GPT teacher head0.222
Teacher spread0.179 · 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 source (direct Gemma or distilled Codex), 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

Citations0
Published2017
Admission routes1
Has abstractyes

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