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Vulnerability of Northern Prairie Wetlands to Climate Change

2005· article· en· W2110532700 on OpenAlexaboutno aff
W. Carter Johnson, Bruce Millett, Tagir G. Gilmanov, Richard A. Voldseth, Glenn R. Guntenspergen, David E. Naugle

Bibliographic record

VenueBioScience · 2005
Typearticle
Languageen
FieldEnvironmental Science
TopicPeatlands and Wetlands Ecology
Canadian institutionsnot available
FundersU.S. Geological SurveyNature ConservancyDesert Research InstituteU.S. Environmental Protection Agency
KeywordsWetlandWaterfowlGrasslandClimate changeEnvironmental scienceHabitatVegetation (pathology)EcosystemGeographyEcosystem servicesAgricultureEcology

Abstract

fetched live from OpenAlex

The prairie pothole region (PPR) lies in the heart of North America and contains millions of glacially formed, depressional wetlands embedded in a landscape matrix of natural grassland and agriculture. These wetlands provide valuable ecosystem services and produce 50% to 80% of the continent's ducks. We explored the broad spatial and temporal patterns across the PPR between climate and wetland water levels and vegetation by applying a wetland simulation model (WETSIM) to 18 stations with 95-year weather records. Simulations suggest that the most productive habitat for breeding waterfowl would shift under a drier climate from the center of the PPR (the Dakotas and southeastern Saskatchewan) to the wetter eastern and northern fringes, areas currently less productive or where most wetlands have been drained. Unless these wetlands are protected and restored, there is little insurance for waterfowl against future climate warming. WETSIM can assist wetland managers in allocating restoration dollars in an uncertain climate future.

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)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.039
Threshold uncertainty score0.078

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
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 head0.021
GPT teacher head0.260
Teacher spread0.240 · 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

Citations396
Published2005
Admission routes1
Has abstractyes

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