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Record W3019449764 · doi:10.1139/cjfr-2019-0200

Growth of canopy red oak near its northern range limit: current trends, potential drivers, and implications for the future

2020· article· en· W3019449764 on OpenAlexvenueno aff
Rebecca L. Stern, Paul G. Schaberg, Shelly A. Rayback, Paula F. Murakami, Christopher F. Hansen, Gary J. Hawley

Bibliographic record

VenueCanadian Journal of Forest Research · 2020
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicTree-ring climate responses
Canadian institutionsnot available
Fundersnot available
KeywordsGrowing seasonCanopyBasal areaPrecipitationEnvironmental scienceFagaceaeRange (aeronautics)EcologyClimate changePhysical geographyBiologyGeographyMeteorology

Abstract

fetched live from OpenAlex

Red oak (Quercus rubra L.) is projected to expand into the northern hardwood forest over the coming century. We explored the connection between red oak basal area growth and a number of factors (tree age and size, stand dynamics, site elevation, and climate and acid deposition variables) for 213 trees in 11 plots throughout Vermont, USA. Red oak growth generally increased over the course of the chronology (1935–2014) and has been particularly high in recent decades. Growth differed among elevational groups but did not differ between age or size groups. Summer moisture metrics were consistently and positively associated with growth, whereas fall moisture was associated with reduced growth in recent decades. Higher summer temperatures were often negatively associated with growth, though there was evidence that low temperatures in the summer (higher elevations) and fall (lower elevations) constrain growth. Several pollution metrics were associated with reduced growth, a surprising result for a species not known to be sensitive to inputs of acid deposition that have predisposed other species in the region to decline. While red oak growth is currently robust, increases in summer temperatures, reductions in growing season precipitation, or increases in fall precipitation could reduce future growth potential.

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)0.000
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 score0.476
Threshold uncertainty score0.937

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.055
GPT teacher head0.294
Teacher spread0.239 · 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

Citations7
Published2020
Admission routes1
Has abstractyes

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