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Record W2075565470 · doi:10.1002/ece3.731

Evidence for 20th century climate warming and wetland drying in the <scp>N</scp>orth <scp>A</scp>merican <scp>P</scp>rairie <scp>P</scp>othole <scp>R</scp>egion

2013· article· en· W2075565470 on OpenAlexaboutno aff
Brett Werner, W. Carter Johnson, Glenn R. Guntenspergen

Bibliographic record

VenueEcology and Evolution · 2013
Typearticle
Languageen
FieldEnvironmental Science
TopicPeatlands and Wetlands Ecology
Canadian institutionsnot available
FundersU.S. Geological SurveyOffice of Research and DevelopmentU.S. Environmental Protection Agency
KeywordsWetlandEnvironmental scienceEcosystemClimate changeGlobal warmingGrasslandVegetation (pathology)BiodiversityFlood mythGroundwater rechargeEcologyGeographyBiologyGroundwater

Abstract

fetched live from OpenAlex

The Prairie Pothole Region (PPR) of North America is a globally important resource that provides abundant and valuable ecosystem goods and services in the form of biodiversity, groundwater recharge, water purification, flood attenuation, and water and forage for agriculture. Numerous studies have found these wetlands, which number in the millions, to be highly sensitive to climate variability. Here, we compare wetland conditions between two 30-year periods (1946-1975; 1976-2005) using a hindcast simulation approach to determine if recent climate warming in the region has already resulted in changes in wetland condition. Simulations using the WETLANDSCAPE model show that 20th century climate change may have been sufficient to have a significant impact on wetland cover cycling. Modeled wetlands in the PPR's western Canadian prairies show the most dramatic effects: a recent trend toward shorter hydroperiods and less dynamic vegetation cycles, which already may have reduced the productivity of hundreds of wetland-dependent species.

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)0.001
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 score0.933
Threshold uncertainty score0.133

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
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 integrity0.0010.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.017
GPT teacher head0.240
Teacher spread0.223 · 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

Citations74
Published2013
Admission routes1
Has abstractyes

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