DNA barcoding as a useful tool in the systematic study of wild bees of the tribe Augochlorini (Hymenoptera: Halictidae)
Bibliographic record
Abstract
Special care is needed in the delimitation and identification of halictid bee species, which are renowned for being morphologically monotonous. Corynura Spinola and Halictillus Moure (Halictidae: Augochlorini) contain species that are key elements in southern South American ecosystems. These bees are very difficult to identify due to close morphological similarity among species and high sexual dimorphism. We analyzed 170 barcode-compliant COI sequences from 19 species. DNA barcodes were useful to confirm gender associations and to detect two new cryptic species. Interspecific distances were significantly higher than those reported for other bees. Maximum intraspecific divergence was less than 1% in 14 species. Barcode index numbers (BINs) were useful to identify putative species that need further study. More than one BIN was assigned to five species. The name Corynura patagonica (Cockerell) probably refers to two cryptic species. The results suggest that Corynura and Halictillus species can be identified using DNA barcodes. The sequences of the species included in this study can be used as a reference to assess the identification of unknown specimens. This study provides additional support for the use of DNA barcodes in bee taxonomy and the identification of specimens, which is particularly relevant in insects of ecological importance such as pollinators.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".