BOLD’s role in barcode data management and analysis: a response
Bibliographic record
Abstract
Abstract DNA barcoding is a very effective tool for the identification of specimens when a carefully validated and taxonomically comprehensive library of reference DNA barcodes is available. Libraries meeting this criterion are now available for some taxonomic groups in some geographic regions, provoking their use as a tool for the identification of samples that would otherwise remain as unknowns. In this article, we emphasize the need for caution in the interpretation of identifications based on a reference library with entries that have seen limited validation. We also emphasize the need for the deposition of sequence records for ‘unknowns’ so that presumptive identifications can be tested by other researchers and updated as the barcode reference library gains increased coverage and validation.
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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 | 0.079 | 0.248 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.005 | 0.011 |
| Scholarly communication | 0.010 | 0.015 |
| Open science | 0.007 | 0.007 |
| Research integrity | 0.033 | 0.032 |
| Insufficient payload (model declined to judge) | 0.020 | 0.010 |
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".