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Record W2975658376 · doi:10.2737/nrs-gtr-p-186-paper8

Sugar maple decline and lessons learned about Allegheny Plateau soils and landscapes

2019· book-chapter· en· W2975658376 on OpenAlexaboutno aff
Robert P. Long, Stephen B. Horsley, Scott W. Bailey, Richard A. Hallett, Thomas J. Hall

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldEnvironmental Science
TopicForest ecology and management
Canadian institutionsnot available
Fundersnot available
KeywordsMapleTwigMarshTree healthAceraceaeYellow birchPlateau (mathematics)Crown (dentistry)SugarGeographyForestryEcologyBiologyBotanyWetlandMedicine

Abstract

fetched live from OpenAlex

Sugar maple (Acer saccharum Marsh.) decline was a major forest health challenge in northern Pennsylvania starting in the mid- to late 1980s and continued through the mid-1990s. During this time sugar maple suffered extensive crown dieback and rapid mortality, primarily on the unglaciated Allegheny Plateau in northwestern and north-central Pennsylvania (Drohan et al. 2002, Horsley et al. 2000). Declining trees exhibited a slow loss of vigor and increased fine twig dieback, which was followed by large branch mortality. This frequently ended in tree death (Kolb and McCormick 1993). Surveys conducted as part of the North American Maple Project found sugar maple healthy in most parts of its range, though declines in Quebec were noted in the mid- and late 1980s (Allen et al. 1992, 1995, 1999). In northern Pennsylvania a series of stressors, which included insect defoliators and extreme drought, played a significant role in accelerating crown dieback and mortality.

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: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.032
Threshold uncertainty score0.064

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0030.003
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0080.001

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 head0.016
GPT teacher head0.237
Teacher spread0.221 · 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

Citations3
Published2019
Admission routes1
Has abstractyes

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