DNA barcoding the vascular plant flora of southern British Columbia
Bibliographic record
Abstract
DNA barcoding is a tool for rapidly identifying species based on short, standardized sequences of DNA, for example in situations where this may be difficult using morphology alone. I assembled a core DNA barcode reference library for southern British Columbia, home to ~54% of the vascular plant species in Canada, using the core plastid barcode loci rbcL and matK, and assessed its utility for identifying species in this region. The library comprises 4,812 sequences obtained from field-collected and herbarium tissue samples, supplemented with sequences downloaded from BOLD and GenBank, with at least one sequence for 75.4% of the vascular plant species occurring below 50°N in British Columbia. Sequence recoverability was significantly higher for rbcL than for matK (93.5% and 80.2%, respectively), and only marginally lower for both markers when using herbarium specimens (90.5% for rbcL and 77.8% for matK), which demonstrates the future feasibility of using museum specimens for completing a southern BC barcode reference library. As a proxy for assessing marker effectiveness, I scored resolution at the level of species and genus using tests of monophyly for Neighbour Joining trees, and performed sequence similarity searches with BLASTn analyses, both for each locus separately and for a dual-locus marker system (rbcL+matK; scored as a cumulative percentage in the BLAST analyses). Ignoring species represented by singleton sequences, the highest overall level of discrimination (66.9% of species and 91.6% of genera) was achieved for BLASTn analysis of rbcL+matK together. This work represents a significant contribution to a nation-wide barcode database, and provides a preliminary platform for ecological and other applications requiring species identification, where traditional methods are not feasible.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.007 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".