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Record W4213155319 · doi:10.5194/hessd-8-6533-2011

Effects of peatland drainage management on peak flows

2011· preprint· en· W4213155319 on OpenAlexaff
C. E. Ballard, Neil McIntyre, H. S. Wheater

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicPeatlands and Wetlands Ecology
Canadian institutionsGlobal Institute for Water SecurityUniversity of Saskatchewan
FundersEngineering and Physical Sciences Research Council
KeywordsDrainagePeatDitchBlocking (statistics)Hydrology (agriculture)Environmental scienceGeologyGeographyEcologyGeotechnical engineeringComputer science

Abstract

fetched live from OpenAlex

Abstract. Open ditch drainage has historically been a common land management practice in upland blanket peats, particularly in the UK. However, peatland drainage is now generally considered to have adverse effects on the upland environment, including increased peak flows. As a result, drain blocking has become a common management strategy in the UK over recent years, although there is only anecdotal evidence to suggest that this might decrease peak flows. The change in the hydrological regime associated with the drainage of blanket peat and the subsequent blocking of drains is poorly understood, therefore a new physics-based model has been developed that allows the exploration of the associated hydrological processes. A series of simulations is used to explore the response of intact, drained and blocked drain sites at field scales. While drainage is generally found to increase peak flows, the effect of drain blocking appears to be dependent on local conditions, sometimes decreasing and sometimes increasing peak flows. Based on insights from these simulations we propose guidelines for identifying those drains that would most greatly reduce peak flows if blocked.

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)0.001
metaresearch head score (Gemma)0.002
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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.008
GPT teacher head0.207
Teacher spread0.200 · 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

Citations3
Published2011
Admission routes1
Has abstractyes

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