Species assemblage of buprestid beetles in four hardwood cover types in Michigan
Bibliographic record
Abstract
Information on species assemblages of metallic wood-boring beetles (Coleoptera: Buprestidae) in forested habitats in North America is relatively scarce, likely reflecting the difficulty of effectively trapping and accurately identifying species. We identified buprestid species captured on four baited traps placed in each of 12 sites representing four common forest cover types in five Michigan counties. Overstory vegetation was dominated by ash (Fraxinus spp.), maple (Acer spp.), oak (Quercus spp.), or poplar (Populus spp.) trees (three sites per cover type). A total of 1656 buprestids representing 28 species were captured on sticky purple prism traps baited with either cis-3-hexenol plus Manuka oil or 3R-hydroxyhexane-2-one plus ethanol from May to August 2014. Buprestid species richness ranged from 6 to 13 species per site. PERMANOVA results showed that buprestid species composition differed among forest cover types (P < 0.005). The invasive Agrilus sulcicollis Lacordaire and the native Chrysobothris femorata Olivier species group were significant indicators of oak sites, while Agrilus obsoletoguttatus Gory was a significant indicator of maple sites. Nonmetric multidimensional scaling showed that abundance of captured buprestids was influenced by availability of fresh snags and coarse woody debris. Our results indicate that trapping can provide an efficient means to assess assemblages of phloem- or wood-boring buprestids.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.001 | 0.000 |
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".