Exploring the effecacy, utility, and limitations of DNA barcoding within the class aves
Bibliographic record
Abstract
This thesis investigates the efficacy of a recently proposed molecular bioidentifcation system known as "DNA barcoding". This system employs a short, standardized gene region (648bp of the mitochondrial gene cytochrome ' c' oxidase I, in the case of animals) as a unique species identifier. To test species-level resolution, I constructed a library of DNA barcode sequences for birds from three regions: the Nearctic (North America), the southern Neotropics (Argentina), and the eastern Palearctic (Russia, Mongolia, and Kazakhstan). The accuracy of barcode-based species identification was assessed using the currently accepted avian taxonomy, which is the most robust of any taxonomic group. I also tested the use of DNA barcodes for species discovery via detection of large intraspecific divergences. Common intraspecific and interspecific trends in phylogeography were compared within and between biogeographical realms. Using the avian barcode library, I also compared the performance of several different methods for species delimitation, including distance-based thresholds, tree-based methods, and character-based methods (wherein each nucleotide of the sequence is treated as a unique character). Finally, I used the abundance of sequence data to test for signs of selection in cytochrome ' c' oxidase I. Whole mitochondrial genomes available from GenBank were used to review the consistency of selective pressure throughout the genome. This largely confirmed the role of purifying selection in the evolution of the mitochondrial genome in birds. Overall, this study substantiates the utility of DNA barcoding as a reliable tool for the purposes of species identification and for highlighting taxa in need of further taxonomic review.
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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".