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Record W2133339100 · doi:10.1139/x05-185

Tree growth response to climate change at the deciduousboreal forest ecotone, Ontario, Canada

2005· article· en· W2133339100 on OpenAlexvenueaboutno aff
David Goldblum, Lesley S. Rigg

Bibliographic record

VenueCanadian Journal of Forest Research · 2005
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicTree-ring climate responses
Canadian institutionsnot available
FundersNational Oceanic and Atmospheric AdministrationNorthern Illinois UniversityNational Geographic SocietyWisconsin Space Grant Consortium
KeywordsAbies balsameaBalsamEcotoneDeciduousTaigaUnderstoryClimate changeMapleBorealPrecipitationEnvironmental scienceEcologyGlobal warmingBlack spruceForestryMarshGeographyBiologyWetlandBotanyShrubCanopy

Abstract

fetched live from OpenAlex

We consider the implications of climate change on the future of the three dominant forest species, sugar maple (Acer saccharum Marsh.), white spruce (Picea glauca (Moench) Voss), and balsam fir (Abies balsamea (L.) Mill.), at the deciduous–boreal forest ecotone, Ontario, Canada. Our analysis is based on individual species responses to past monthly temperature and precipitation conditions in light of modeled (general circulation model) monthly temperature and precipitation conditions in the study area for the 2080s. We then consider the tree species sensitivity to past climate with predicted conditions for the 2080 period. Sugar maple, located at its northern limit in the study area, shows the greatest potential for increased growth rates under the predicted warming and altered precipitation regime. White spruce is likely to benefit less, while the understory dominant balsam fir is likely to experience a decrease in growth potential. These projected changes would enhance the future status of sugar maple at its northern limit and facilitate range expansion northward in response to global warming.

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 score0.015
Threshold uncertainty score0.088

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.047
GPT teacher head0.277
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

Citations128
Published2005
Admission routes2
Has abstractyes

Explore more

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