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Record W4394436060 · doi:10.6084/m9.figshare.6213785

Beaulieu C, Lavoie C, Proulx R. 2018. Bookkeeping of insect herbivory trends in herbarium specimens of purple loosestrife (Lythrum salicaria).

2018· dataset· en· W4394436060 on OpenAlexaboutno aff
Raphaël Proulx, Caroline Beaulieu, Claude Lavoie

Bibliographic record

VenueFigshare · 2018
Typedataset
Languageen
FieldEnvironmental Science
TopicForest Insect Ecology and Management
Canadian institutionsnot available
Fundersnot available
KeywordsHerbariumBiologyInsectBotany

Abstract

fetched live from OpenAlex

The potential use of herbarium specimens to detect herbivory trends is enormous but largely untapped. The objective of this study was to reconstruct the long-term herbivory pressure on an invasive plant, purple loosestrife (<i>Lythrum salicaria</i>), by evaluating leaf damages over 1323 specimens from southern Québec (Canada). Historical trends suggest a gradual increase in hole feeding and margin feeding damages from 1883 to around 1940, followed by a period of relative stability. The percentage of specimens with window feeding damages did not begin to increase until the end of the 20th century, from 3% (2 ̶ 6%) in 1990 to 45% (14 ̶ 81%) in 2015. Temporal changes in the frequency of window feeding damages support the hypothesis of an increasing herbivory pressure by recently introduced insects. This study shows that leaf damages made by insects introduced for the biocontrol of purple loosestrife, such as coleopterans of the <i>Neogalerucella</i> genus, can be assessed from voucher specimens. Herbaria are a rich source in information that can be used to answer questions related to plant-insect interactions in the context of biological invasions and biodiversity changes.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.336
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.002
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.3380.002

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 head0.031
GPT teacher head0.252
Teacher spread0.222 · 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 designNot applicable
Domainnot available
GenreDataset

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

Citations1
Published2018
Admission routes1
Has abstractyes

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