Assessing the Value of DNA Barcodes and Other Priority Gene Regions for Molecular Phylogenetics of Lepidoptera
Bibliographic record
Abstract
BACKGROUND: Despite apparently abundant amounts of observable variation and species diversity, the order Lepidoptera exhibits a morphological homogeneity that has provided only a limited number of taxonomic characters and led to widespread use of nucleotides for inferring relationships. This study aims to characterize and develop methods to quantify the value of priority gene regions designated for Lepidoptera molecular systematics. In particular, I assess how the DNA barcode segment of the mitochondrial COI gene performs across a broad temporal range given its number one position of priority, most sequenced status, and the conflicting opinions on its phylogenetic performance. METHODOLOGY/PRINCIPAL FINDINGS: Gene regions commonly sequenced for lepidoptera phylogenetics were scored using multiple measures across three categories: practicality, which includes universality of primers and sequence quality; phylogenetic utility; and phylogenetic signal. I found that alternative measures within a category often appeared correlated, but high scores in one category did not necessarily translate into high scores in another. The DNA barcode was easier to sequence than other genes, and had high scores for utility but low signal above the genus level. CONCLUSIONS/SIGNIFICANCE: Given limited financial resources and time constraints, careful selection of gene regions for molecular phylogenetics is crucial to avoid wasted effort producing partially informative data. This study introduces an approach to assessing the value of gene regions prior to the initiation of new studies and presents empirical results to help guide future selections.
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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| 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".