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Record W2153743907 · doi:10.1139/x03-008

Indirect effects of beech bark disease on sugar maple seedling survival

2003· article· en· W2153743907 on OpenAlexvenueno aff
Elizabeth Hane

Bibliographic record

VenueCanadian Journal of Forest Research · 2003
Typearticle
Languageen
FieldEnvironmental Science
TopicForest ecology and management
Canadian institutionsnot available
Fundersnot available
KeywordsBeechMapleSeedlingYellow birchSugarAceraceaeBark (sound)ShadingBotanyBiologyHorticultureExperimental forestEcology

Abstract

fetched live from OpenAlex

To investigate the mechanisms of indirect effects of the increased presence of American beech (Fagus grandifolia Ehrh.) saplings on sugar maple (Acer saccharum Marsh.) seedling survival, I conducted several experiments in the area of the Hubbard Brook Experimental Forest in central New Hampshire, U.S.A. To investigate the effects of competition from beech saplings on sugar maple seedlings, a removal experiment was conducted. Sugar maple seedling survival was monitored in five replicate plots of each of the two treatments for 6 years. Survivorship in plots in which beech saplings had been removed was significantly higher (33%) than in control plots (1%). A shading experiment demonstrated that a large proportion of the mortality of sugar maple seedlings results from the effects of shading. Cutting and shade cloth treatments were done in a two-factor factorial block design, and results showed a strong negative effect of shading in the plot. A third experiment investigated the role of soil moisture. Plots that had higher soil moisture and also had beech removed had the highest survival (76%), while control plots in a dry area had the lowest (22%). Overall, the experiments showed that beech bark disease and the associated increase in beech saplings had a negative indirect effect on sugar maple seedling survival. Sugar maple regeneration failure appeared to be, at least in part, due to the indirect effects of beech bark disease.

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)0.002
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.081
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
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)0.0010.000

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 head0.270
Teacher spread0.250 · 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.

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

Citations105
Published2003
Admission routes1
Has abstractyes

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