Supplementary material from "Bookkeeping of insect herbivory trends in herbarium specimens of purple loosestrife (<i>Lythrum salicaria</i>)"
Bibliographic record
Abstract
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 the Eurasian invasive plant, purple loosestrife (Lythrum salicaria), by evaluating leaf damage over 1323 specimens from southern Québec (Canada). The hypothesis tested is that that the prevalence of herbivory damage on purple loosestrife is low during the invasion phase and increases throughout the saturation phase. Historical trends suggest a gradual increase in hole feeding and margin feeding damage from 1883 to around 1940, followed by a period of relative stability. The percentage of specimens with window feeding damage did not begin to increase until the end of the twentieth century, from 3% (2–6%) in 1990 to 45% (14–81%) in 2015. Temporal changes in the frequency of window feeding damage support the hypothesis of an increasing herbivory pressure by recently introduced insects. This study shows that leaf damage made by insects introduced for the biocontrol of purple loosestrife, such as coleopterans of the Neogalerucella 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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.639 | 0.199 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".