Identifying sharks with DNA barcodes: assessing the utility of a nucleotide diagnostic approach
Bibliographic record
Abstract
Shark fisheries worldwide are mostly unmanaged, but the burgeoning shark fin industry in the last few decades has made monitoring catch and trade of these animals critical. As a tool for molecular species identification, DNA barcoding offers significant potential. However, the genetic distance-based approach towards species identification employed by the Barcode of Life Data Systems may oftentimes lack the specificity needed for regulatory or legal applications that require unambiguous identification results. This is because such specificity is not typically realized by anything less than a 100% match of the query sequence to an entry in the reference database using genetic distance. Although various divergence thresholds have been proposed to define acceptable levels of intraspecific variation, enough exceptions exist to cast reasonable doubt on many less than exact matches using a distance-based approach for the identification of unknowns. An alternative approach relies on the identification of discrete molecular characters that can be used to unambiguously diagnose species. The objective of this study was to assess the performance differences between these competing approaches by examining more than 1000 DNA barcodes representing nearly 20% of all known elasmobranch species. Our results demonstrate that a character-based, nucleotide diagnostic (ND) approach to barcode identification is feasible and also provides novel insights into the structure of haplotype diversity among closely related species of sharks. Considerations for the use of NDs in applied fields are also explored.
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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 | 0.008 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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 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".