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Record W4399860071 · doi:10.1139/facets-2023-0207

An integrated assessment of impacts to ecosystem services associated with prairie pothole wetland drainage quantifying wide-ranging losses

2024· article· en· W4399860071 on OpenAlexafffundvenueabout
Colin J. Whitfield, Emily Cavaliere, Helen M. Baulch, Robert G. Clark, Christopher Spence, Kevin Shook, Zhihua He, John W. Pomeroy, Jared D. Wolfe

Bibliographic record

VenueFACETS · 2024
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicSoil erosion and sediment transport
Canadian institutionsEnvironment and Climate Change CanadaGlobal Institute for Water SecurityUniversity of Saskatchewan
FundersGlobal Water FuturesNatural Sciences and Engineering Research Council of Canada
KeywordsPothole (geology)WetlandEnvironmental scienceEcosystem servicesEcosystemRangingDrainageLand useRemote sensingEnvironmental resource managementHydrology (agriculture)Water resource managementAgroforestryEcologyGeographyGeology

Abstract

fetched live from OpenAlex

In many regions, a tradeoff exists between draining wetlands to support the expansion of agricultural land, and conserving wetlands to maintain their valuable ecosystem services. Decisions about wetland drainage are often made without identifying the impacts on the services these systems provide. We address this gap through a novel assessment of impacts on ecosystem services via wetland drainage in the Canadian prairie landscape. Draining pothole wetlands has large impacts, but sensitivity varies among the indicators considered. Loss of water storage increased the magnitude of median annual flows, but absolute increases with drainage were higher for larger, less frequent events. Total phosphorus exports increased in concert with streamflow. Our analysis suggested disproportionate riparian habitat losses with the first 30% of wetland area drained. Dabbling ducks and wetland-associated bird abundances respond strongly to the loss of small wetland ponds; abundances were predicted to decrease by half with the loss of only 20%–40% of wetland area. This approach to evaluating changes to key wetland ecosystem services in a large region where wetland drainage is ongoing can be used with an economic valuation of the drainage impacts, which should be weighed against the benefits associated with agricultural expansion.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.540
Threshold uncertainty score0.565

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.024
GPT teacher head0.282
Teacher spread0.258 · 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.

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

Citations9
Published2024
Admission routes4
Has abstractyes

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