Bibliographic record
Abstract
The promise of DNA barcoding is based on a small DNA fragment divergence coinciding with biological species separation. Here we evaluated the performance of three markers as diatom barcodes, the small ribosomal subunit (1600 bp), a 5' end fragment of cytochrome c oxidase subunit 1 (430 bp), and the second internal transcribed spacer region combined with the 5.8S gene (5.8S + ITS-2, 300-400 bp). Forty-four sequences per marker representing 28 species from all diatom classes were analysed. Sequence alignment of the three genetic markers and uncorrected genetic distances (P) were calculated at the intra- and heterospecific level. All three markers correctly separated the species examined and had advantages which contribute to their feasibility as a DNA barcode. Small ribosomal subunit had the largest GenBank data set, its success rate in amplification and sequencing was assumed to be the highest of all three and was readily aligned. However, it required a long fragment to recover divergence sufficient for species separation and small genetic distances increased the potential for misidentifications. Cytochrome c oxidase subunit 1 demonstrated a substantial heterospecific divergence level and was also readily alignable, but it showed very low amplification and sequencing success rates with currently existing primers. 5.8S + ITS-2 was amplified and sequenced with high success rate and was the most variable of the three markers, but its secondary structure was needed to aid in alignment. However, since it has been recently suggested that ITS-2 may provide insight into sexual compatibility, this marker offers an additional advantage. We therefore propose that the 5.8S + ITS-2 fragment is the best candidate as a diatom DNA barcode.
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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 | 0.005 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.006 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".