MétaCan
Menu
Back to cohort
Record W2945980045

A Forest Death in Three Acts: From Woodpeckers to Water, the Emerald Ash Borer's Effects are Widespread

2019· article· en· W2945980045 on OpenAlexaboutno aff
Kathleen S. Knight, Charles E. Flower, Mark D. Nelson

Bibliographic record

VenueThe Wildlife Professional · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicForest Insect Ecology and Management
Canadian institutionsnot available
Fundersnot available
KeywordsEmerald ash borerAgrilusFraxinusBuprestidaeFirewoodRiparian forestBark (sound)EcologyGeographyAgroforestryBiologyRiparian zoneHabitat
DOInot available

Abstract

fetched live from OpenAlex

Dead treetops, their bare branches curving upward, stood dark against a blue sky. Only a few years before, these had been towering, vibrant ash trees, playing a vital role in riparian forest ecosystems of northwest Ohio. Then, the emerald ash borer (Agrilus planipennis), a shiny green beetle whose voracious maggot-like larva feeds just beneath the bark of the trees, swept through the forests leaving death and destruction in its wake. The emerald ash borer, or EAB, is native to eastern Asia. There, it has little impact on Asian ash species, which are able to mount defenses against the larvae. But in the late 1990s, it was accidentally introduced to North America, arriving near Detroit. Michigan, on wooden shipping material. When EAB reached the nearby forests, it encountered North American ash species that had no co-evolutionary history with the pest and almost no defense. The insect was inadvertently spread by people, moving on firewood, logs and even vehicles. Now, it occurs in 35 U.S. states and five Canadian provinces. Our job was to monitor the effects of this invasive pest. including the decline and death of the ash trees, and the cascading effects of the sudden loss of these tree species on their forest ecosystems.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.023
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.004

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 head0.009
GPT teacher head0.231
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; both teacher heads agree on what is shown here.

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

Citations0
Published2019
Admission routes1
Has abstractyes

Explore more

Same venueThe Wildlife ProfessionalSame topicForest Insect Ecology and ManagementFrench-language works237,207