MétaCan
Menu
Back to cohort
Record W2976174918 · doi:10.1139/facets-2018-0051

Distinguishing eastern North American forest moth pests by wing-scale ultrastructure: potential applications in paleoecology

2019· article· en· W2976174918 on OpenAlexaffvenue
Kristen Jane Milbury, Les C. Cwynar, Sara Edwards

Bibliographic record

VenueFACETS · 2019
Typearticle
Languageen
FieldEnvironmental Science
TopicFire effects on ecosystems
Canadian institutionsForest Protection Limited (Canada)University of New Brunswick
Fundersnot available
KeywordsSpruce budwormChoristoneura fumiferanaBiologyAbies balsameaLepidoptera genitaliaEcologyPaleoecologyTortricidaeBotanyBalsam

Abstract

fetched live from OpenAlex

The use of fossil moth wing scales has recently been introduced as a new method to reconstruct population histories of lepidopterans and provide a proxy for insect disturbance. We investigated the potential for using wing-scale ultrastructure to distinguish between the five most common outbreak species of moth pests in eastern North America: spruce budworm ( Choristoneura fumiferana Clemens), hemlock looper ( Lambdina fiscellaria Guenée), forest tent caterpillar ( Malacosoma disstria Hübner), blackheaded budworm ( Acleris variana Fernie), and jack pine budworm ( Choristoneura pinus Freeman). Using scanning electron images of scales, we made qualitative and quantitative comparisons of morphological traits at the ultrastructural level. We found that hemlock looper and eastern blackheaded budworm scales could be categorically separated from each other and from the three other species. We developed a quadratic discriminant function using measurements of ultrastructure traits that distinguishes scales of the three remaining species with an overall accuracy of 66%. We found that forest tent caterpillar could be well separated based on these traits, but we were less confident in distinguishing the closely related jack pine and spruce budworm. Our method offers potential advantages in scale identification for future studies in paleoecology, while providing the additional advantage of not requiring intact, unfolded, and undamaged scales.

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)0.000
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 score0.026
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.002
GPT teacher head0.189
Teacher spread0.187 · 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

Citations3
Published2019
Admission routes2
Has abstractyes

Explore more

Same venueFACETSSame topicFire effects on ecosystemsFrench-language works237,207