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Record W2895428482 · doi:10.13140/rg.2.2.12825.47205

Insights into mountain wetland resilience to climate change: An evaluation of the hydrological processes contributing to the hydrodynamics of alpine wetlands in the Canadian Rocky Mountains

2018· dissertation· en· W2895428482 on OpenAlexaboutno aff
Jason J. Mercer

Bibliographic record

Venuenot available
Typedissertation
Languageen
FieldEnvironmental Science
TopicPeatlands and Wetlands Ecology
Canadian institutionsnot available
Fundersnot available
KeywordsWetlandClimate changeEnvironmental scienceResilience (materials science)GeographyHydrology (agriculture)Physical geographyEnvironmental resource managementEcologyGeologyOceanography

Abstract

fetched live from OpenAlex

Hydrological conditions play an important role in provisioning the exceptionally valuable\necosystem services and functions of wetlands. In alpine areas, wetland functions and services are\nexpected to be very sensitive to climate-mediated changes in hydrology. However, few field\nstudies of alpine wetland hydrology currently exist, thus limiting understanding of how wetlands\nwill respond to warming and drying, and how their ecosystem services and functions will\nchange. This study examines key processes contributing to the hydrological stability of alpine\nwetlands in Banff National Park, AB, Canada. During the two-year study, snowmelt timing\ndiffered by over three weeks, allowing for the examination of water table patterns under\ncomparatively wet and dry conditions. Contrary to expectations, water table positions were\nrelatively stable in each study year, particularly in the peat-bearing soils. Hydrophysical and\nhydrochemical data together provide evidence that the observed stability is in part due to\ngroundwater contributions, which made up as much as 53% of the water budget in one peatland.\nSoil conditions also appear to play a role in stabilizing water table regimes. The results suggest\nthat alpine wetlands, and peatlands in particular, may be more resilient to changes in climate than\ncurrently thought. Mineral wetlands, comparatively, may have limited adaptive capacity.

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 score0.082
Threshold uncertainty score0.164

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
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 head0.017
GPT teacher head0.287
Teacher spread0.270 · 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

Citations4
Published2018
Admission routes1
Has abstractyes

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