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Record W2887710131 · doi:10.1080/15230430.2018.1495445

Topographic influences on ring widths of trees and shrubs across alpine treelines in southwest Yukon

2018· article· en· W2887710131 on OpenAlexafffundabout
Katherine D. Dearborn, Ryan K. Danby

Bibliographic record

VenueArctic Antarctic and Alpine Research · 2018
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicTree-ring climate responses
Canadian institutionsQueen's University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsSubarctic climateShrubDendrochronologyPrecipitationClimate changeTree lineDendroclimatologyEnvironmental scienceEcologyPhysical geographyGeographyBiology

Abstract

fetched live from OpenAlex

Growth responses of trees and shrubs to climate often exhibit unexplained variation in alpine regions, making it difficult to predict how they will respond to future changes in climate. We sought to characterize and explain this variability in southwest Yukon, a topographically complex region of subarctic Canada. We collected cores and sections from 360 spruce trees and 480 willow shrubs across treelines on north and south aspects in six valleys spanning two mountain ranges. We compared growth rates, growth patterns, and climate-growth responses between species and topographic factors. South aspects had wider tree rings and higher tree and shrub interseries correlations than north aspects, likely because of shallow active layers on the latter. Growth patterns and responses to climate did not vary between aspects or elevations but differed slightly between mountain ranges, likely because of differences in spring soil moisture content between ranges. Growth responses of both species to summer temperature were positive, but tree growth was negatively correlated to spring temperature and shrub growth was negatively correlated to summer precipitation, both of which are projected to increase along with summer temperature. Future changes in climate could therefore reduce the growth of one or both species in southwest Yukon.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation 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 score0.040
Threshold uncertainty score0.978

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.002
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.053
GPT teacher head0.349
Teacher spread0.296 · 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.

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

Citations12
Published2018
Admission routes3
Has abstractyes

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