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Record W4405313052 · doi:10.1139/cjfr-2024-0114

Climate dominates geographic influences on whitebark pine and limber pine trends and landscape patterns in Canada

2024· article· en· W4405313052 on OpenAlexafffundvenueabout
Brenda Shepherd, Jodie Krakowski, Iain Neill Reid, Michael P. Murray, Natalie Stafl, Robert Sissons, Genoa Alger, Jane Park, Charlie McLellan, Carl J. Schwarz, Cyndi M. Smith

Bibliographic record

VenueCanadian Journal of Forest Research · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Insect Ecology and Management
Canadian institutionsMount Revelstoke National ParkMinistry of ForestsGovernment of British ColumbiaGovernment of CanadaParks Canada
FundersUniversity of AlbertaParks CanadaGovernment of AlbertaAlberta Environment and Parks
KeywordsMountain pine beetleBiologyEcologyLatitudeDendroctonusResistance (ecology)ForestryGeographyBark beetleBark (sound)

Abstract

fetched live from OpenAlex

Limber pine (LP; Pinus flexilis) and whitebark pine (WBP; Pinus albicaulis) are classified as endangered in Canada due to rapid declines caused by the introduced pathogen causing white pine blister rust (WPBR; Cronartium ribicola), mountain pine beetle, and other stressors. A long-term monitoring study from 2003 to 2019 on 102 LP and 232 WBP permanent plots found that LP mortality decreased with increasing latitude and spring solar radiation, while WBP mortality was highest at low latitudes and elevations in areas with higher moisture and longer growing degree days. WPBR incidence in LP was associated with lower latitudes, high spring precipitation, cool/wet summers, and low solar radiation, while WBP disease incidence drivers were similar, plus increasing tree diameter and slope. Annually, mature LP and WBP mortality from all causes increased 0.4% and 0.5%, respectively, while disease incidence in live trees increased 0.6% and 0.5%. Regeneration density increased 1.3% annually on average for WBP and 2.5% for LP. Disease incidence and mortality rates have slowed compared to prior assessments, likely due to a recruitment deficit of healthy trees and some natural selection for blister rust resistance. The results support customizing landscape restoration strategies based on each species’ unique biology and local climatic factors that influence mortality and infection.

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)0.001
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 score0.012
Threshold uncertainty score0.084

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.001
Scholarly communication0.0010.000
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 head0.012
GPT teacher head0.258
Teacher spread0.246 · 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

Citations2
Published2024
Admission routes4
Has abstractyes

Explore more

Same venueCanadian Journal of Forest Research→Same topicForest Insect Ecology and Management→French-language works237,207→