Using Herbarium‐Derived DNAs to Assemble a Large‐Scale DNA Barcode Library for the Vascular Plants of Canada
Bibliographic record
Abstract
Premise of the study: Constructing complete, accurate plant DNA barcode reference libraries can be logistically challenging for large‐scale floras. Here we demonstrate the promise and challenges of using herbarium collections for building a DNA barcode reference library for the vascular plant flora of Canada. Methods: Our study examined 20,816 specimens representing 5076 of 5190 vascular plant species in Canada (98%). For 98% of the specimens, at least one of the DNA barcode regions was recovered from the plastid loci rbcL and matK and from the nuclear ITS2 region. We used beta regression to quantify the effects of age, type of preservation, and taxonomic affiliation (family) on DNA sequence recovery. Results: Specimen age and method of preservation had significant effects on sequence recovery for all markers, but influenced some families more (e.g., Boraginaceae) than others (e.g., Asteraceae). Discussion: Our DNA barcode library represents an unparalleled resource for metagenomic and ecological genetic research working on temperate and arctic biomes. An observed decline in sequence recovery with specimen age may be associated with poor primer matches, intragenomic variation (for ITS2), or inhibitory secondary compounds in some taxa.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.003 | 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) | 0.004 | 0.002 |
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".