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Record W2039511702 · doi:10.1139/x11-138

Changes in ash tree demography associated with emerald ash borer invasion, indicated by regional forest inventory data from the Great Lakes States

2011· article· en· W2039511702 on OpenAlexvenueno aff
Scott A. Pugh, Andrew M. Liebhold, Randall S. Morin

Bibliographic record

VenueCanadian Journal of Forest Research · 2011
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Insect Ecology and Management
Canadian institutionsnot available
Fundersnot available
KeywordsEmerald ash borerFraxinusAgrilusGeographyEcologyForest inventoryForestryBiologyForest management

Abstract

fetched live from OpenAlex

The emerald ash borer (EAB) ( Agrilus planipennis Fairmaire) is a nonnative phloem-feeding beetle that was accidentally introduced near Detroit, Michigan, two to three decades ago. North American ash ( Fraxinus spp.) exhibit little or no resistance, and as this insect species expands its range, extensive mortality results. Previous studies of the impacts of EAB, typical of most insect and disease impact studies, utilized data acquired from sites with known infestations and cannot be used to make regional estimates of change on forest land. By contrast, this study investigated the regional impacts of EAB on the affected resource using information from a large-scale forest inventory (Forest Inventory and Analysis program of the US Department of Agriculture, Forest Service) previously implemented to estimate regional forest resources. Results indicate that since 1980, ash has been increasing throughout many of the Great Lakes States but EAB is reversing this trend in recently invaded areas. Within 50 km of the epicenter of the EAB invasion, a major decline was observed after 2004. For growing stock (trees at least 12.7 cm diameter at breast height), average ash volume decreased from 12.7 to 3.2 m 3 ·ha –1 and mortality increased from 0.1 to 1.4 m 3 ·ha –1 ·year –1 on timberland between the 2004 and 2009 inventories.

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 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 score0.845
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.087
GPT teacher head0.268
Teacher spread0.181 · 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

Citations59
Published2011
Admission routes1
Has abstractyes

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