A response to: Evaluating the reliability of DNA Barcoding for Central American Pacific shallow water echinoderms identification
Bibliographic record
Abstract
Introduction: Chacón-Monge et al. (2024) sought to test the accuracy of DNA barcoding for species identification in Pacific Central American shallow water echinoderms. They used cytochrome c oxidase I (COI) sequences derived from new material collected as part of the BioMar-ACG project in Costa Rica. Using their set of 348 echinoderm sequences, they compared species identification results from two online platforms: the National Center for Biotechnology Information (NCBI) GenBank using the nucleotide Basic Local Alignment Search Tool (BLASTn), and the Barcode of Life Data Systems (BOLD) Identification Engine. Objective: The present article is a response to their results and conclusions. Methods: We reinterpreted the results from the authors’ Appendix 2 to enable an objective comparison between the BOLD Identification Engine and BLASTn in GenBank. Results: While the authors found that both platforms were limited by the number of reference sequences available in their respective databases, they concluded that GenBank outperformed BOLD for identification; however, we identify several methodological flaws in their analysis. These include pseudoreplication amongst query sequences, contaminated sequences stemming from sampling errors, and a lack of standardization when interpreting results from the two platforms. Their assessment of the BOLD Identification Engine was also limited by improper selection of a reference database. Conclusion: Addressing these errors, we reinterpret their results and demonstrate that there is no difference in performance between the two platforms.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 teacher head, 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".