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Record W2124231655 · doi:10.1139/er-2014-0048

Ecological benefits and risks arising from liming sugar maple dominated forests in northeastern North America

2014· article· en· W2124231655 on OpenAlexaffvenueabout
Jean‐David Moore, Rock Ouimet, Robert P. Long, Paul A. Bukaveckas

Bibliographic record

VenueEnvironmental Reviews · 2014
Typearticle
Languageen
FieldAgricultural and Biological Sciences
TopicForest Ecology and Biodiversity Studies
Canadian institutionsMinistère des Ressources naturelles et des Forêts
Fundersnot available
KeywordsEcosystemEcologyEnvironmental scienceHabitatYellow birchForest ecologyMapleAceraceaeAgroforestryBiology

Abstract

fetched live from OpenAlex

Liming, the application of carbonate materials (e.g., CaCO 3 , CaMg(CO 3 ) 2 ) to soils and surface waters, has been used extensively in Europe, and to a lesser extent in Canada and the United States, to mitigate the effects of acid deposition on forest and aquatic ecosystems. This literature review was conducted to assess the effects of liming on ecologically and economically important sugar maple dominated ecosystems of northeastern North America, where it is increasingly used to treat sugar maple dieback. Potential direct and indirect effects were considered to determine whether the use of liming to revitalize these forests could negatively affect other ecological parameters, including those in adjacent aquatic habitats. Based on current scientific literature, it is not anticipated that liming at rates of 1–3 t ha −1 would have major detrimental effects on these ecosystems. However, liming could have negative effects on northern hardwood forests with regard to earthworm invasions. The choice of liming as a mitigation tool should be made not only after weighing the potentially negative effects against the benefits of restoring sugar maple dominated stands in poorly buffered soils, but also after considering ecological components that could be lost or never recovered if an acidified forest ecosystem is not limed.

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)0.000
metaresearch head score (Gemma)0.000
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 score0.262
Threshold uncertainty score0.728

Codex and Gemma teacher scores by category

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 head0.032
GPT teacher head0.212
Teacher spread0.179 · 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

Citations44
Published2014
Admission routes3
Has abstractyes

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