Automating the Curation of <scp>DNA</scp> Barcode Databases for Vascular Plants
Bibliographic record
Abstract
ABSTRACT Comprehensive, curated, and current DNA barcode reference databases are essential for both the identification of single specimens and for the interpretation of metabarcoding data. In the case of plants, nuclear (ITS) and plastid (rbcL, matK) markers are commonly used together. Because the plastid regions are segments of protein‐coding genes, their alignment and analysis are usually straightforward. By contrast, the assembly and validation of ITS records is considerably more difficult for two reasons: the prevalence of indels and intraindividual sequence variation. This complexity has provoked the development of several workflows to support the curation of reference databases for the internal transcribed spacer (ITS) region for plant barcoding. However, the pipelines used to create these databases lack functionalities which are essential to ensure a solid post‐analytical validation. This paper presents a new workflow to address these shortcomings, with the goal of enhancing the reliability and accuracy of plant barcoding studies. We furthermore demonstrate that clustering of reference databases results in a substantial drop in the fraction of queries that gain a correct species‐level assignment. By contrast, setting an acceptance threshold for identifications, based on the distance between query and match, leads to a meaningful reduction of error rates in incomplete reference databases.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".