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Record W2128162388 · doi:10.1139/cjfr-2013-0415

Condition of green ash (<i>Fraxinus pennsylvanica</i>) overstory and regeneration at three stages of the emerald ash borer invasion wave

2014· article· en· W2128162388 on OpenAlexvenueno aff
Stephen J. Burr, Deborah G. McCullough

Bibliographic record

VenueCanadian Journal of Forest Research · 2014
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Insect Ecology and Management
Canadian institutionsnot available
FundersCore Research for Evolutional Science and TechnologyMichigan Department of Natural Resources
KeywordsEmerald ash borerFraxinusAgrilusBiologyBasal areaCanopyBotanyEcologyForestryGeography

Abstract

fetched live from OpenAlex

Assessing emerald ash borer (Agrilus planipennis Fairmaire) impacts in North American forests is essential for projecting future species composition of stands invaded by this phloem-feeding pest. We surveyed all species of overstory trees and regeneration in 2010 and 2011 in 24 forested sites with a major component of green ash (Fraxinus pennsylvanica), representing the Core, Crest, and Cusp of the A. planipennis invasion wave across southern Michigan. By 2011, an average of 78.6% ± 0.10%, 44.8% ± 0.11%, and 19.8% ± 0.07% of overstory ash trees representing 87%, 57%, and 14% of the total ash basal area had been killed in Core, Crest, and Cusp sites, respectively. Green ash seedlings, saplings, and recruits were abundant in all sites, but newly germinated ash seedlings were absent in Core sites and scarce in Crest sites. Canopy gaps resulting from current ash decline and mortality increased available photosynthetically active radiation (PAR) and sapling growth in Crest sites, but PAR was low in Core and Cusp sites. Lateral ingrowth of non-ash overstory trees has largely filled canopy gaps in Core sites, and there was little evidence of green ash recruitment into the overstory. Green ash appears unlikely to persist as a dominant species in forests invaded by A. planipennis.

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)0.000
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 score0.726
Threshold uncertainty score0.985

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
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 head0.029
GPT teacher head0.252
Teacher spread0.223 · 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

Citations61
Published2014
Admission routes1
Has abstractyes

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