DNA barcoding of Canada’s skates
Bibliographic record
Abstract
DNA-based identifications have been employed across broad taxonomic ranges and provide an especially useful tool in cases where external identification may be problematic. This study explored the utility of DNA barcoding in resolving skate species found in Atlantic Canadian waters. Most species were clearly resolved, expanding the utility for such identification on a taxonomically problematic group. Notably, one genus (Amblyraja) contained three of four species whose distributions do not overlap that could not be readily identified with this method. On the other hand, two common and partially sympatric species (Little and Winter skates) were readily identifiable. There were several instances of inconsistency between the voucher identification and the DNA sequence data. In some cases, these were at the intrageneric level among species acknowledged to be prone to misidentification. However, several instances of intergeneric discrepancies were also identified, suggesting either evidence of past introgressive hybridization or misidentification of vouchered specimens across broader taxonomic ranges. Such occurrences highlight the importance of retaining vouchered specimens for subsequent re-examination in the light of conflicting DNA evidence.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".