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Record W2939605032 · doi:10.1139/cjfr-2018-0360

Spatial variation in oak (<i>Quercus</i> spp.) radial growth responses to drought stress in eastern North America

2019· article· en· W2939605032 on OpenAlexvenueno aff
David C. LeBlanc, Adam Berland

Bibliographic record

VenueCanadian Journal of Forest Research · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicPlant Water Relations and Carbon Dynamics
Canadian institutionsnot available
FundersU.S. Department of Energy
KeywordsPrecipitationGrowing seasonClimate changeEvapotranspirationGeographySpatial variabilityEnvironmental scienceWater balancePhysical geographyEcologyBiologyGeology

Abstract

fetched live from OpenAlex

Dendroecology provides a means to evaluate how mature trees have responded to climate stresses in the recent past and provides one approach for projecting how existing forests will respond to future climate change. This study documented spatial variation in the strength of growth–climate associations for six oak (Quercus) species at 284 sites in eastern North America that span substantial gradients of temperature and site water balance. Radial growth of oaks was more strongly related to growing-season precipitation and the ratio of precipitation to potential evapotranspiration at sites in the western part of the study region where drought conditions occur more frequently. Growth was more strongly related to growing-season mean maximum temperature in the warmer, southern part of the study region. Growth of oaks was not strongly related to site water balance or temperature in the northeastern part of the study region. These results indicate that if climate change results in increased growing-season drought stress, this will adversely affect mature oak trees growing in the southern and western parts of eastern North America, but oaks growing in northeastern North America have more safe space for change before they will suffer reduced growth and vigor.

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 score0.050
Threshold uncertainty score0.100

Distilled classifier scores by category (both heads)

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)0.0000.000

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 head0.014
GPT teacher head0.243
Teacher spread0.230 · 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

Citations9
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Journal of Forest Research→Same topicPlant Water Relations and Carbon Dynamics→French-language works237,207→