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Record W2088252876 · doi:10.1657/aaar0013-076

Vegetation Recovery Patterns Following Permafrost Disturbance in a Low Arctic Setting: Case Study of Herschel Island, Yukon, Canada

2015· article· en· W2088252876 on OpenAlexaffabout
Heather Cray, Wayne H. Pollard

Bibliographic record

VenueArctic Antarctic and Alpine Research · 2015
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsMcGill University
Fundersnot available
KeywordsTundraForbPermafrostVegetation (pathology)ThermokarstArctic vegetationEcologyEnvironmental scienceArcticEcosystemPlant communityShrubDisturbance (geology)LitterSlumpPhysical geographyGeographyEcological successionGeologyGrasslandBiology

Abstract

fetched live from OpenAlex

The potential long-term effects of thaw slump disturbances on tundra ecosystems include a significant reorganization of tundra plant communities. In this study, vegetation sucession of stabilized retrogressive thaw slumps was investigated using four age classes: 10, 20, and 250 years old, and undisturbed areas. While still active, slumps are characterized by bare soil, pools of water and liquefied mud, and the initial colonization of graminoids and forbs. Between 0 and 20 years after stabilization, the slump vegetation is characterized by low diversity, grass-dominated vegetation communities with a high percent cover of dry bare ground. This is followed after a few hundred years by a forb, dwarf shrub, and bryophyte-dominated community with a high percent cover of litter. With future warming expected, the presence and areal extent of thaw disturbances are predicted to increase. These findings suggest that the result would be an altered tundra species composition, which remains distinct for at least several centuries.

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)0.000
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 score0.034
Threshold uncertainty score0.080

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0010.001
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 head0.060
GPT teacher head0.307
Teacher spread0.247 · 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

Citations53
Published2015
Admission routes2
Has abstractyes

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