Development of a DNA Barcoding Protocol for Fungal Specimens from the E.C. Smith Herbarium (ACAD)
Bibliographic record
Abstract
Many field-collected fungal specimens are maintained in herbaria worldwide. These specimens contain an untapped wealth of taxonomic and ecological fungal biodiversity information. However, DNA can be difficult to obtain from preserved specimens. We present a DNA barcoding protocol specifically for preserved fungal specimens (ascomycetes and basidiomycetes). The E.C. Smith Herbarium at Acadia University houses 20,000 fungal specimens representative of northeastern North America. We achieved a DNA barcoding success rate of 18% from pre-1980 specimens (n = 39) using a kit-based DNA extraction protocol and sequencing of the full internal transcribed spacer (ITS) region of ribosomal DNA. This result surpassed success rates of previous protocols. We also explored the use of mini-barcodes from the ITS1 region only. Mini-barcodes (n = 13) demonstrated a 92% success rate in post-1980 specimens compared to full barcodes (46% success rate, n = 13) while retaining all the identification power of full barcodes in the examined specimens. Our approach will enable herbarium collections to be used more efficiently to populate DNA sequence databases such as GenBank. This approach will expand the number of reference DNA barcode sequences from vouchered fungal specimens within publicly available databases.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.008 |
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".