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Record W3167871735 · doi:10.1002/eco.2316

Ecohydrological metrics for vegetation communities in turloughs (ephemeral karstic wetlands)

2021· article· en· W3167871735 on OpenAlexaff
Saheba Bhatnagar, Laurence Gill, Steve Waldren, Nova Sharkey, Owen Naughton, Paul Johnston, Catherine Coxon, Patrick Morrissey, Bidisha Ghosh

Bibliographic record

VenueEcohydrology · 2021
Typearticle
Languageen
FieldEnvironmental Science
TopicHydrology and Watershed Management Studies
Canadian institutionsTrinity College
FundersU.S. Environmental Protection Agency
KeywordsWetlandHydrology (agriculture)Environmental scienceVegetation (pathology)Flood mythKarstPlant communityHabitatEphemeral keyPhysical geographyGeographyEcologyGeologyEcological succession

Abstract

fetched live from OpenAlex

Abstract A 28‐year hydrological record on four intermittent wetlands (turloughs) in a hydraulically linked karst area in the west of Ireland was used to assess ecohydrological metrics for different vegetation communities. A methodology using a combination of continuous water level monitoring and high resolution topographic surveying was used to develop a detailed hydrological model of the karst network, from which water levels at any point within the turloughs can be defined at any time during the 28‐year period (1989 to 2017). The flood conditions experienced across the spatial distributions for different vegetation communities (as mapped by a field survey) have then been collated and presented as statistical distributions for flood duration, flood depth, flood frequency and mean temperature/global radiation at the time of year in spring when the flood waters start to recede. Analysis of these four turloughs has revealed distinct differences between vegetation communities, from Eleocharis acicularis communities at the turlough base typically experiencing 6 to 7 months of inundation per year compared to the limestone pavement community at the top fringes of the turloughs only flooded from 1 to 2 months per year. An approach that used Sentinel‐2 satellite data to provide an assessment of whether there have been changes in the spatial distribution of the communities is also presented. Such metrics can be evaluated alongside other variables such as water quality (particularly nutrients), soil type and land‐use, in order to understand the habitat requirements for such plant communities and their associated ecological systems.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.552
Threshold uncertainty score1.000

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)0.0010.000

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 head0.021
GPT teacher head0.247
Teacher spread0.226 · 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

Citations6
Published2021
Admission routes1
Has abstractyes

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