Global Availability of Plant <scp>DNA</scp> Barcodes as Genomic Resources to Support Basic and Policy‐Relevant Biodiversity Research
Bibliographic record
Abstract
Genetic technologies such as DNA barcoding make it easier and less expensive to monitor biodiversity and its associated ecosystem services, particularly in biodiversity hotspots where traditional assessments are challenging. Successful use of these data-driven technologies, however, requires access to appropriate reference data. We reviewed the >373,584 reference plant DNA barcodes in public repositories and found that they cumulatively cover a remarkable quarter of the ~435,000 extant land plant species (Embryophyta). Nevertheless, coverage gaps in tropical biodiversity hotspots reflect well-documented biases in biodiversity science - most reference specimens originated in the Global North. Currently, at least 17% of plant families lack any reference barcode data whatsoever, affecting tropical and temperate regions alike. Investigators often emphasise the importance of marker choice and the need to ensure protocols are technically capable of detecting and identifying a broad range of taxa. Yet persistent geographic and taxonomic gaps in the reference datasets show that these protocols rely upon risk undermining all downstream applications of the strategy, ranging from basic biodiversity monitoring to policy-relevant objectives - such as the forensic authentication of materials in illegal trade. Future networks of investigators could work strategically to improve data coverage, which will be essential in global efforts to conserve biodiversity while advancing more fair and equitable access to benefits arising from genetic resources.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".