Evaluating the capacity of plant <scp>DNA</scp> barcodes to discriminate species of cotton (<i><scp>G</scp>ossypium</i>:<scp> M</scp>alvaceae)
Bibliographic record
Abstract
Although two plastid regions have been adopted as the standard markers for plant DNA barcoding, their limited resolution has provoked the consideration of other gene regions, especially in taxonomically diverse genera. The genus Gossypium (cotton) includes eight diploid genome groups (A-G, and K) and five allotetraploid species which are difficult to discriminate morphologically. In this study, we tested the effectiveness of three widely used markers (matK, rbcL, and ITS2) in the discrimination of 20 diploid and five tetraploid species of cotton. Sequences were analysed locus-wise and in combinations to determine the most effective strategy for species identification. Sequence recovery was high, ranging from 92% to 100% with mean pairwise interspecific distance highest for ITS2 (3.68%) and lowest for rbcL (0.43%). At a 0.5% threshold, the combination of matK+ITS2 produced the greatest number of species clusters. Based on 'best match' analysis, the combination of matK+ITS2 was best, while based on 'all species barcodes' analysis, ITS2 gave the highest percentage of correct species identifications (98.93%). The combination of sequences for all three markers produced the best resolved tree. The disparity index test based on matK+rbcL+ITS2 was significant (P < 0.05) for a higher number of species pairs than the individual gene sequences. Although all three barcodes separated the species with respect to their genome type, no single combination of barcodes could differentiate all the Gossypium species, and tetraploid species were particularly difficult.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".