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Record W1969291275 · doi:10.2747/0272-3646.27.4.297

High Arctic Patchy Wetlands: Hydrologic Variability and Their Sustainability

2006· article· en· W1969291275 on OpenAlexaff
Ming‐ko Woo, Kathy L. Young, Laura C. Brown

Bibliographic record

VenuePhysical Geography · 2006
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsYork UniversityMcMaster University
Fundersnot available
KeywordsPermafrostWetlandSnowmeltEnvironmental scienceThermokarstArcticHydrology (agriculture)TundraClimate changeSnowEcologyGeologyOceanographyGeomorphology

Abstract

fetched live from OpenAlex

Wetlands in the polar desert environment of the High Arctic provide a special ecological niche for the tundra plants, insects, birds, and animals. Non-tidal patchy wetlands exhibit a hydrologic regime that includes a high water table and extensive flooding in the snowmelt period, followed by a gradual summer drying that is interrupted by rainfall events that raise water levels. These wetlands are also prone to inter-annual variations in wetness. Given sufficient local water supply, they are self-sustaining entities in which soil saturation favors ground ice formation, but the ice-rich permafrost prevents deep percolation while vegetation growth and peat development further insulate the ground. Several processes can alter this balance: a change in drainage can reduce inflow; excessive melting of ground ice leading to thermokarst can modify wetland morphology and flow pattern. On a regional scale, projected climatic warming of the Arctic may extend the thawed season, enhance evaporation, and eliminate the late-lying snowbanks that feed some patchy wetlands. Under such a scenario, patchy wetlands in the High Arctic are considered to be highly vulnerable.

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 score0.014
Threshold uncertainty score0.028

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 studies0.0000.000
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 head0.008
GPT teacher head0.202
Teacher spread0.194 · 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

Citations23
Published2006
Admission routes1
Has abstractyes

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