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Record W3045952615 · doi:10.1002/ecs2.3118

Leading‐edge disequilibrium in alder and spruce populations across the forest–tundra ecotone

2020· article· en· W3045952615 on OpenAlexafffund
Hana Travers-Smith, Trevor C. Lantz

Bibliographic record

VenueEcosphere · 2020
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicClimate change and permafrost
Canadian institutionsUniversity of Victoria
FundersNatural Sciences and Engineering Research Council of CanadaArcticNet
KeywordsEcotoneTundraAlderEcologyDisequilibriumVegetation (pathology)Physical geographyEnvironmental scienceAbundance (ecology)Species distributionRange (aeronautics)Climate changeGeographyArcticBiologyHabitat

Abstract

fetched live from OpenAlex

Abstract The distribution and composition of Arctic vegetation are expected to shift with ongoing climate change. Global models generally predict northward shifts in high‐latitude ecotones, and analysis of remote sensing data shows widespread greening and changes in vegetation structure across the circumpolar Arctic. However, there are still uncertainties related to the timing of these shifts and variation among different plant functional types. In this paper, we investigate disequilibrium dynamics of green alder and white spruce in the Tuktoyaktuk Coastal Plain, NWT. We used high‐resolution air photographs captured in the 1970s and 2000s to quantify changes in the distribution and abundance of alder and spruce near their northern limits. We found increases in alder and spruce stem density over time, but no change in their range limits, indicating that both species are affected by leading‐edge disequilibrium. Low stand density and temperature limitation of reproduction along the northern margin likely contributed to observed disequilibrium in both species. We also observed the greatest change in species occupancy within a burned area, suggesting that the increased frequency of fire will play a significant role in the timing and magnitude of near‐term vegetation change.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.235
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0080.001

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 head0.061
GPT teacher head0.277
Teacher spread0.216 · 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 teacher head, not a consensus.

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

Citations28
Published2020
Admission routes2
Has abstractyes

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