DNA barcoding a regional bee (Hymenoptera: Apoidea) fauna and its potential for ecological studies
Bibliographic record
Abstract
DNA barcoding has been evaluated for many animal taxa and is now advocated as a reliable and rapid means for species-level identification. The coming-to-light of this identification tool is timely as we are now facing perhaps the greatest rate of species loss in recent millennia. This study contributes to an ever-increasing number of published accounts of DNA barcoding successfully and accurately distinguishing animal taxa, in this instance, the bee fauna of Nova Scotia, Canada. Most members of this well-known fauna were resolved with particular clarity; the average intraspecific divergence was less than 0.5%, and COI sequences from over 75% of the province's species are now in the Barcodes of Life Data System. DNA barcoding also revealed some surprises within this fauna, including the possible recognition of two undescribed genetically unique species, one in the genus Ceratina (subgenus Zadontomerus), the second in the genus Andrena (subgenus Larandrena); both are presently receiving further taxonomic study. In addition, DNA barcoding has allowed sex-associations among two pairs of cleptoparasitic species. The resulting utility of DNA barcoding for ecological studies of bee communities is discussed.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".