The Emerald Ash Borer: Beautifully Deadly
Bibliographic record
Abstract
The Jewel Beetles (Coleoptera: Buprestidae) contain ~15,000 of the most stunning yet least understood species in the animal kingdom (Fig. 1; Bellamy and Nelson, 2002). Although it is the 8th largest beetle family and contains many species of biological interest and economic importance, the family is poorly studied. To date, only one molecular phylogeny has been published for the family, and the six subfamilial classifications are still in flux (Evans et al. 2014). Of particular interest within the family Buprestidae is the Emerald Ash Borer; (EAB; Agrilus planipennis) a destructive pest of great economic importance (Fairmare). The EAB was introduced from Asia into North American ash forests in 2002 and has quickly spread to fifteen states and two Canadian provinces, becoming the primary destroyer of ash trees throughout the region (Vannatta et al. 2012). Studies have shown that male EABs find mates and ash trees primarily through visual cues, and seem to be particularly attracted to purple traps (Lelito et al. 2008). Due to the lack of a robust familial phylogeny, very little is known about the relationship of the EAB to other members within the family Buprestidae, which includes other potential pests that could cause similar economic impacts if introduced into North American forests. This study creates a molecular phylogeny to give context in which to study Buprestid visual systems and identify other potential pests. In addition, this study uses transcriptomics to understand EAB visual systems at the molecular level. Traditionally, beetles only have two opsin copies: one long wavelength (LW) and one UV. Due to the highly visual nature of the EAB as compared to other beetles, we aim to see if there are additional opsin copies and where the EAB visual spectrum is most sensitive.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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.
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".