Untangling taxonomy: a <scp>DNA</scp> barcode reference library for <scp>C</scp>anadian spiders
Bibliographic record
Abstract
Approximately 1460 species of spiders have been reported from Canada, 3% of the global fauna. This study provides a DNA barcode reference library for 1018 of these species based upon the analysis of more than 30,000 specimens. The sequence results show a clear barcode gap in most cases with a mean intraspecific divergence of 0.78% vs. a minimum nearest-neighbour (NN) distance averaging 7.85%. The sequences were assigned to 1359 Barcode index numbers (BINs) with 1344 of these BINs composed of specimens belonging to a single currently recognized species. There was a perfect correspondence between BIN membership and a known species in 795 cases, while another 197 species were assigned to two or more BINs (556 in total). A few other species (26) were involved in BIN merges or in a combination of merges and splits. There was only a weak relationship between the number of specimens analysed for a species and its BIN count. However, three species were clear outliers with their specimens being placed in 11-22 BINs. Although all BIN splits need further study to clarify the taxonomic status of the entities involved, DNA barcodes discriminated 98% of the 1018 species. The present survey conservatively revealed 16 species new to science, 52 species new to Canada and major range extensions for 426 species. However, if most BIN splits detected in this study reflect cryptic taxa, the true species count for Canadian spiders could be 30-50% higher than currently recognized.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".