Contribution of DNA barcoding to the study of the bees (Hymenoptera: Apoidea) of Canada: progress to date
Bibliographic record
Abstract
Abstract Bees (Hymenoptera: Apoidea, Apiformes) are taxonomically and ecologically diverse, with a wide range of social complexity, nesting preferences, floral associations, and biogeographic restrictions. A Canadian bee checklist, greatly assisted by the gene-assisted approach of DNA barcoding, is nearing completion. Previous evaluation of bee diversity in Canada, assisted by DNA barcoding, was restricted to Nova Scotia, which contains about 25% of the bee species in the country. Here, we summarise efforts to date to build a comprehensive DNA barcode library supporting bee taxonomic studies in Canada, consisting of more than 12 500 barcode-compliant sequences yielding 811 distinct barcode index numbers (BINs). This appears to represent ~95% of the 856 bee species presently recorded from Canada, but comparison with known morphological species in each genus shows that some genera are still under-sampled or may contain cryptic taxa, with much taxonomic work still to be done on bees in Canada. This is particularly true within the taxonomically difficult genera Andrena Fabricius (Andrenidae) , Hylaeus Fabricius (Colletidae), Melissodes Latreille (Apidae), Nomada Scopoli (Apidae), Osmia Panzer (Megachilidae), and Sphecodes Latreille (Halictidae). DNA analysis will likely be a key asset in resolving bee taxonomic issues in Canada in the future, and to date has even assisted studies of well-known bee taxa. Here we present summaries of our results, and discuss the use of DNA barcoding to assist future taxonomic work, faunal lists, and ecological studies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".