Database of Vascular Plants of Canada (VASCAN): a community contributed taxonomic checklist of all vascular plants of Canada, Saint Pierre and Miquelon, and Greenland
Bibliographic record
Abstract
The Database of Vascular Plants of Canada or VASCAN (http://data.canadensys.net/vascan) is a comprehensive and curated checklist of all vascular plants reported in Canada, Greenland (Denmark), and Saint Pierre and Miquelon (France). VASCAN was developed at the Université de Montréal Biodiversity Centre and is maintained by a group of editors and contributors. For every core taxon in the checklist (species, subspecies, or variety), VASCAN provides the accepted scientific name, the accepted French and English vernacular names, and their synonyms/alternatives in Canada, as well as the distribution status (native, introduced, ephemeral, excluded, extirpated, doubtful or absent) of the plant for each province or territory, and the habit (tree, shrub, herb and/or vine) of the plant in Canada. For reported hybrids (nothotaxa or hybrid formulas) VASCAN also provides the hybrid parents, except if the parents of the hybrid do not occur in Canada. All taxa are linked to a classification. VASCAN refers to a source for all name, classification and distribution information. All data have been released to the public domain under a CC0 waiver and are available through Canadensys and the Global Biodiversity Information Facility (GBIF). VASCAN is a service to the scientific community and the general public, including administrations, companies, and non-governmental organizations.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.018 | 0.033 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.037 | 0.010 |
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".