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Record W2766217601 · doi:10.3390/f8110413

Low Light Availability Associated with American Beech Is the Main Factor for Reduced Sugar Maple Seedling Survival and Growth Rates in a Hardwood Forest of Southern Quebec

2017· article· en· W2766217601 on OpenAlexafffundabout
Alexandre Collin, Christian Messier, Steven W. Kembel, Nicolas Bélanger

Bibliographic record

VenueForests · 2017
Typearticle
Languageen
FieldEnvironmental Science
TopicFire effects on ecosystems
Canadian institutionsUniversité TÉLUQUniversité du Québec en OutaouaisUniversité du Québec à Montréal
FundersNatural Sciences and Engineering Research Council of CanadaUniversité de Montréal
KeywordsMapleBeechAceraceaeYellow birchUnderstorySeedlingBiologyMicrositeHardwoodBotanyAgronomyCanopy

Abstract

fetched live from OpenAlex

Several recent studies have reported a marked increase in American beech dominance (Fagus grandifolia Ehrh.) relative to sugar maple (Acer saccharum Marsh.) in late successional forests of North America. However, many factors have been proposed to explain this sudden shift in tree species composition. We investigated the microsite factors responsible for maple regeneration failure under maple-beech stands, focusing on both light availability and soil conditions. The survival and growth of maple seedlings planted in the natural soil and in pots with enriched soil were monitored for two years, as well as foliar nutrition and herbivory damages of natural seedlings. The results indicate that low light availability associated with the presence of beech is the primary factor leading to maple regeneration failures. Soil nutrient availability and foliar nutrition of natural seedlings did not differ between forest types. Yet, the results indicate that factors such as allelopathy and preferential herbivory on maple seedlings under beech could be superimposed effects that hinder maple regeneration. Under similar forests, a control of beech sapling abundance in the understory followed by selection cutting could be one way to promote and maintain maple populations in the longer term.

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 score0.639
Threshold uncertainty score0.959

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 head0.009
GPT teacher head0.226
Teacher spread0.217 · 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

Citations27
Published2017
Admission routes3
Has abstractyes

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