Comment on “Acquiring DNA sequence data from dried archival red algae (Florideophyceae) for the purpose of applying available names to contemporary genetic species: a critical assessment” <sup>1</sup>Appears in Botany <b>90</b>(3): 191–203. doi:10.1139/b11-079.
Bibliographic record
Abstract
Saunders and McDevit recently reported their efforts to extract and amplify DNA by PCR in successively older red algal (Rhodophyta) herbarium specimens. They found that recent collections (4-11 years old) readily amplified but that archival material (decades to a century old) yielded contamination problems, diminished success correlated with age, or failed to amplify. As a solution, they proposed that epitypes be designated based on contemporary sequenced specimens. In response, we extracted and amplified in independent laboratories three loci (COI, ITS2, and rbcL) from the same 1836 Sparlingia pertusa specimen that Saunders and McDevit were unable to amplify. The use of Q-solution enhanced amplification success and likely is partly responsible for our achievements with archival specimens. These findings, along with data from the last 13 years in which we have sequenced over 100 historical and type specimens, indicate that with proper controls, amplifying DNA from red algal herbarium specimens of any age is practical and reproducible. The designation of contemporary epitypes should be a last resort, not an alternative to sequencing type material, and must be done with an understanding of the historical record of the species.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.031 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.033 | 0.032 |
| Insufficient payload (model declined to judge) | 0.008 | 0.012 |
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".