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Record W4379746416 · doi:10.1002/fee.2654

Biological invasion threatens keystone species indelibly entwined with Indigenous cultures

2023· review· en· W4379746416 on OpenAlexaboutno aff
Nathan W. Siegert, Deborah G. McCullough, Thomas Luther, Les Benedict, Susan J. Crocker, Kelly Church

Bibliographic record

VenueFrontiers in Ecology and the Environment · 2023
Typereview
Languageen
FieldEnvironmental Science
TopicForest Insect Ecology and Management
Canadian institutionsnot available
FundersU.S. Forest ServiceU.S. Department of Agriculture
KeywordsEmerald ash borerIndigenousAgrilusGeographyFraxinusBasal areaInvasive speciesEcologyRange (aeronautics)Introduced speciesBiologyForestry

Abstract

fetched live from OpenAlex

Black ash (Fraxinus nigra), the most highly preferred and vulnerable host of the invasive emerald ash borer (EAB;Agrilus planipennis) in North America, is of cultural and spiritual importance to many Tribal Nations in the US and First Nations in Canada. To date, EAB has invaded nearly 60% of the native range of black ash, with annual spread averaging approximately 50 km per year. On the basis of the predicted expansion of EAB distribution, we estimate that more than 75% of black ash basal area will be lost across 87% of the species’ North American range by 2035. Census data indicate that 98% of Indigenous people currently residing within the geographic range of black ash in the US will be within the area experiencing more than 75% basal area loss by 2035, suggesting broad and multidimensional impacts of EAB invasion for those who value black ash as a cultural keystone species. Collaborative efforts among scientists, resource managers, and Indigenous experts are needed to mitigate EAB impacts and preserve or protect black ash resources, given the species’ vulnerability to EAB and its associated cultural and ecological value.

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.022
GPT teacher head0.229
Teacher spread0.207 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations33
Published2023
Admission routes1
Has abstractyes

Explore more

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