MétaCan
Menu
Back to cohort
Record W2170957004 · doi:10.1139/x05-116

Effects of an introduced pathogen on resistance to natural disturbance: beech bark disease and windthrow

2005· article· en· W2170957004 on OpenAlexvenueno aff
Michael J. Papaik, Charles D. Canham, Erika F. Latty, Kerry D. Woods

Bibliographic record

VenueCanadian Journal of Forest Research · 2005
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Insect Ecology and Management
Canadian institutionsnot available
FundersNational Science Foundation
KeywordsBeechWindthrowTsugaBasal areaBiologyResistance (ecology)EcologyYellow birch

Abstract

fetched live from OpenAlex

In forests of eastern North America, introduced pathogens have caused widespread declines in a number of important tree species, including dominant species such as American beech (Fagus grandifolia Ehrh.). Most studies have focused on changes in forest composition and structure as a direct result of mortality caused by a pathogen. Our field studies of windthrow resistance in forests of northern New York and northern Michigan demonstrate that resistance of beech trees to windthrow is severely reduced by beech bark disease (BBD). This reduced resistance was primarily due to the increase in the probability of stem breaks of moderately and highly infected beech trees. The severity of BBD infection on individual trees has a significant negative effect on resistance to windthrow. We tested potential consequences of this for long-term composition and structure in these forests by using a simulation model, SORTIE. We found that species such as yellow birch (Betula alleghaniensis Britt.) and eastern hemlock (Tsuga canadensis (L.) Carr.) increased in basal area primarily because of the effect BBD had on the creation of new seedbed substrates. Our results highlight the indirect effects that host-specific pathogens can have on community dynamics and species coexistence in forests.

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)0.001
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 score0.305
Threshold uncertainty score0.721

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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 head0.008
GPT teacher head0.257
Teacher spread0.249 · 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

Citations56
Published2005
Admission routes1
Has abstractyes

Explore more

Same venueCanadian Journal of Forest ResearchSame topicForest Insect Ecology and ManagementFrench-language works237,207