DNA barcoding in land plants: evaluation of <i>rbcL</i> in a multigene tiered approach
Bibliographic record
Abstract
DNA barcoding based on the mitochondrial cytochrome c oxidase 1 (cox1) sequence is being employed for diverse groups of animals with demonstrated success in species identification and new species discovery. Applying barcoding systems to land plants will be a more challenging task as plant genome substitution rates are considerably lower than those observed in animal mitochondria, suggesting that a much greater amount of sequence data from multiple loci will be required to barcode plants. In the absence of an obvious well-characterized plant locus that meets all the necessary criteria, a key first step will be identifying candidate regions with the most potential. To meet the challenges with land plants, we are proposing the adoption of a tiered approach wherein highly variable loci are nested under a core barcoding gene. Analysis of over 10 000 rbcL sequences from GenBank demonstrate that this locus could serve well as the core region, with sufficient variation to discriminate among species in approximately 85% of congeneric pair-wise comparisons. Use of a secondary locus can be implemented when required and can vary from group to group if necessary. The implementation of a barcoding tool has multiple academic and practical applications. It will speed routine identifications and the detection of alien species, advance ecological and taxonomic inquiry, permit fast and accurate forensic analysis of plant fragments, and can function as an additional layer of quality control in the food industry.
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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 | 0.006 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".