DNA barcoding a nightmare taxon: assessing barcode index numbers and barcode gaps for sweat bees
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
There is an ongoing campaign to DNA barcode the world's >20 000 bee species. Recent revisions of Lasioglossum (Dialictus) (Hymenoptera: Halictidae) for Canada and the eastern United States were completed using integrative taxonomy. DNA barcode data from 110 species of L. (Dialictus) are examined for their value in identification and discovering additional taxonomic diversity. Specimen identification success was estimated using the best close match method. Error rates were 20% relative to current taxonomic understanding. Barcode Index Numbers (BINs) assigned using Refined Single Linkage Analysis (RESL) and barcode gaps using the Automatic Barcode Gap Discovery (ABGD) method were also assessed. RESL was incongruent for 44.5% of species, although some cryptic diversity may exist. Forty-three of 110 species were part of merged BINs with multiple species. The barcode gap is non-existent for the data set as a whole and ABGD showed levels of discordance similar to the RESL. The viridatum species-group is particularly problematic, so that DNA barcodes alone would be misleading for species delimitation and specimen identification. Character-based methods using fixed nucleotide substitutions could improve specimen identification success in some cases. The use of DNA barcoding for species discovery for standard taxonomic practice in the absence of a well-defined barcode gap is discussed.
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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 | 0.002 | 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) | 0.000 | 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 it