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Record W184444577

DOC export from an upland peat catchment in the Flow Country, northern Scotland

2010· article· en· W184444577 on OpenAlexaboutno aff
Shailaja Vinjili, Ruth Robinson Robinson, Yit Arn Teh, Susan Waldron, Michael J. Singer

Bibliographic record

VenueEGUGA · 2010
Typearticle
Languageen
FieldEnvironmental Science
TopicPeatlands and Wetlands Ecology
Canadian institutionsnot available
Fundersnot available
KeywordsPeatBogEnvironmental scienceHydrology (agriculture)Dissolved organic carbonWater tableDrainage basinGreenhouse gasGroundwaterGeographyGeologyOceanography
DOInot available

Abstract

fetched live from OpenAlex

Flow Country blanket bogs in northern Scotland are the most expansive in Europe covering an area of ∼4000 km, and they significantly impact the global carbon cycle because of their high rates of carbon production and storage, as well as their role in the transfer of carbon to oceans through rivers or greenhouse gas exchange (Moore et al., 1998). These upland areas are highly susceptible to climatic and landuse changes, and currently, large areas of previously drained and forested peatlands are being felled and blocked to increase the water table level and rejuvenate the peatlands (LIFE Peatlands Project 2001-2006; Holden et al., 2004). This study is examining the event-based export of dissolved and particulate organic carbon (DOC and POC) from one of the main upland Flow Country catchments that drains into the north-draining Halladale River. For a time-series of summer rainfall events, we have focussed particularly on a comparison of DOC/POC exports from three different land use areas in the catchment: forested plots, felled to waste (restoration) plots (felled between 2005-2007), and near-pristine bog sites. DOC concentrations have been measured using a combination of methods including TOC and EA analyses, and in situ absorbance measurements using a spectrophotometer (Thurman, 1985; Worrall et al., 2002). Our results show that the stream water draining the felled to waste site records the highest levels of DOC concentration (and DOC variability), and the near-pristine site has the lowest export rate of DOC (and lowest variability). All sites exhibit positive DOC responses to the flood hydrograph, and the near-pristine and forested sites have a similar maximum concentrations of DOC. The felled site concentrations are about 2times greater than the near-pristine and forested sites, and the non-linear response to flow reflects the hydrophobic nature of peats after a period of drought, and the lag time required for them to saturate. The integrated downstream DOC concentrations on forested land and on the main stem of the Halladale River have “forest-like” values reflecting a dilution in DOC concentrations from the felled site, and mixing of stream water from other sources. The initial results from this study imply that i) the felled to waste site (after 2-3 years) releases the highest (up to x2) DOC into stream waters that drain them, ii) DOC concentrations are more sensitive to hydrological variation in sites felled to waste but not yet fully restored, and iii) saturation-excess overland flow is the predominant response of near-pristine site to the rainfall events. References: Holden J., Chapman P.J., and Labadz J.C. 2004. Artificial drainage of peatlands: hydrological and hydrochemical process and wetland restoration. Progress in Phy Geography, 28, 1, pp: 95-123. Life Peatlands Project 2001-2006. www.lifepeatlandsproject.com Moore T.R., Roulet N.T. and Waddington J.M. 1998. Uncertainty in Predicting the Effect of Climatic Change on the Carbon Cycling of Canadian Peatlands. Climate Change, 40, 2, pp: 229-245. Thurman E.M. 1985. Organic Geochemistry of Natural Waters. Netherlands, Martinus Nijhoff/Dr. W.Junk Publishers. Worrall F., Burt T. P., Jaeban R. Y., Warburton ,J. and R. Shedden, 2002. Release of dissolved organic carbon from upland peat. Hydrol. Process. 16, 3487–3504.

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 score0.215
Threshold uncertainty score0.428

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
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 head0.006
GPT teacher head0.222
Teacher spread0.216 · 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
Published2010
Admission routes1
Has abstractyes

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