CAN-SAR: A Database of Canadian Species at Risk Information
Bibliographic record
Abstract
CAN-SAR: A database of Canadian Species at Risk information is an initiative led by Dr. Ilona Naujokaitis-Lewis from Environment Climate Change Canada. The aim of this database is to provide open and accessible data reflecting information obtained from Canadian species at risk listing and recovery planning documents. Ongoing efforts include development of a living database that will facilitate contributions from other parties in an effort to increase efficiencies and decrease multiple (redundant) efforts with the broad over-arching goal of improving the conservation of species at risk. **NOTE:** The current version of CAN-SAR includes documents available as of **March 23, 2021** for species with SARA statuses Endangered, Threatened, and Special Concern. For the authoritative source of current species at risk information please consult the SARA Public Registry (https://www.canada.ca/en/environment-climate-change/services/species-risk-public-registry.html).
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 | 0.002 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.019 | 0.031 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.110 | 0.043 |
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".