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Record W3011657830 · doi:10.1080/15230430.2020.1722397

Influence of climatic conditions on growth rings of <i>Salix uva-ursi</i> Pursh from the southeastern shore of Hudson Bay, Subarctic Canada

2020· article· en· W3011657830 on OpenAlexaffabout
Magdalena Opała‐Owczarek, Piotr Owczarek, Ewa Łupikasza, Stéphane Boudreau, Krzysztof Migała

Bibliographic record

VenueArctic Antarctic and Alpine Research · 2020
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicTree-ring climate responses
Canadian institutionsUniversité LavalCenter for Northern Studies
FundersHorizon 2020
KeywordsSubarctic climateDendroclimatologyBayPrecipitationEnvironmental scienceClimatologyClimate changeDendrochronologyLatitudeFrost (temperature)WillowVegetation (pathology)ShoreTaigaTundraArcticPhysical geographyAtmospheric sciencesOceanographyEcologyGeographyGeologyBiologyMeteorology

Abstract

fetched live from OpenAlex

Over the past decades, warmer air temperature and spatiotemporal changes in the amount and patterns of precipitation have been observed at high latitudes. Such interannual variability in climatic conditions has a strong influence on the dynamics of biological processes regulating terrestrial ecosystems. Dendroclimatology can improve our understanding of the impacts of climate change on vegetation. Based on ring width and frost rings of bearberry willow (Salix uva-ursi) individuals sampled on the southeastern shore of Hudson Bay, Nunavik (Subarctic Québec, Canada), we built a reliable 105-year growth chronology. A complex dendroclimatological analysis, using the standard approach (mean temperature and precipitation sums), extreme variables, and various climatological indices based on daily data, was conducted in order to precisely describe the relationship between annual variations in growth rings and climatic conditions. The growth of Low Arctic willows in the Hudson Bay region is determined by changes in air temperatures as well as precipitation during the summer. However, temperature increases and reductions in amounts of rainfall may intensify drought stress, which is unfavorable for this species. We found that analysis of daily amounts and intensity of precipitation improved our understanding of the factors conditioning the growth of the studied species in an extreme habitat.

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)0.001
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 score0.457
Threshold uncertainty score0.869

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.037
GPT teacher head0.274
Teacher spread0.236 · 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

Citations11
Published2020
Admission routes2
Has abstractyes

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