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Record W2294957175 · doi:10.1139/cjfr-2015-0464

Mortality, structure, and regeneration in whitebark pine stands impacted by mountain pine beetle in the southern Sierra Nevada

2016· article· en· W2294957175 on OpenAlexvenueno aff
Marc D. Meyer, Beverly M. Bulaon, Martin MacKenzie, Hugh D. Safford

Bibliographic record

VenueCanadian Journal of Forest Research · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicFire effects on ecosystems
Canadian institutionsnot available
Fundersnot available
KeywordsMountain pine beetleDendroctonusForestryBiologyRegeneration (biology)EcologyGeographyBark beetle

Abstract

fetched live from OpenAlex

Whitebark pine (Pinus albicaulis Engelm.) is vulnerable to mountain pine beetle (Dendroctonus ponderosae Hopkins) attack throughout western North America, but beetle outbreaks in the southwestern portion of the range (i.e., Sierra Nevada) have been spatially limited until recently. We examined patterns of mortality, structure, and regeneration in whitebark pine stands impacted by mountain pine beetle in the southern Sierra Nevada. Mortality was greatest in medium to large diameter (>10–20 cm dbh) trees, resulting in declines in mean and maximum tree diameter and tree size class diversity following an outbreak. Severity of beetle attack was positively related to mean tree diameter and density. Density of young (<3 years old) whitebark pine seedling clusters was positively related to severity of beetle attack on mature stands. All sites showed a stable production of whitebark pine regeneration within at least the past 30–40 years, with a pulse of new seedlings in the past 3 years in beetle-impacted stands. Our results show that mountain pine beetle outbreaks in the southern Sierra Nevada result in substantial changes in whitebark pine stand structure and suggest low resistance but high resilience to initial attack, especially in the absence of white pine blister rust.

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 score0.083
Threshold uncertainty score0.166

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 head0.020
GPT teacher head0.280
Teacher spread0.260 · 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

Citations19
Published2016
Admission routes1
Has abstractyes

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