Detection of Potential Problematic Cytb Gene Sequences of Fishes in GenBank
Bibliographic record
Abstract
Fishes are, by far, the most diverse group of vertebrates. Their classification relies heavily on morphology. In practice, the correct morphological identification of species often depends on personal experience because many species vary in their body shape, color and other external characters. Thus, the identification of a species may be prone to errors. Due to the rapid development of molecular biology, the number of sequences of fishes deposited in GenBank has grown explosively. These published data likely contain errors owing to invalid or incorrectly identified species. The erroneous data can lead to downstream problems. Thus, it is critical that such errors get identified and corrected. A strategy based on DNA barcoding can detect potentially erroneous data, especially when intraspecific K2P variation exceeds interspecific K2P divergence. Analyses of the most used DNA marker for fishes (mitochondrial Cytb) discovers that intraspecific differences of fishes are generally less than 1%, while interspecific differences are generally higher than 10%. Based on this ruler, our analyses identify 1,303 potential problematic Cytb sequences of fishes in GenBank and point to taxonomic problems, errors in identification, genetic introgression and other concerns. Care must be taken to avoid the perpetuation of errors when using these available data.
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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".