Raising the bar: Recovery ambition for species at risk in Canada and the US
Bibliographic record
Abstract
Routinely crossing international borders and/or persisting in populations across multiple countries, species are commonly subject to a patchwork of endangered species legislation. Canada and the United States share numerous endangered species; their respective acts, the Species at Risk Act (SARA) and the Endangered Species Act (ESA), require documents that outline requirements for species recovery. Although there are many priorities for improving endangered species legislation effectiveness, species recovery goals are a crucial component. We compared recovery goal quality, as measured by goal quantitativeness and ambition, for species listed under SARA and ESA. By comparing across ESA and SARA, the intent of the study was to identify differences and similarities that could support the development of stronger species' recovery goals under both legislations. Our results indicated that: (1) overall, only 38% of recovery goals were quantitative, 41% had high ambition, and 26% were both quantitative and with high ambition; (2) recovery goals had higher quantitativeness and ambition under ESA than SARA; (3) recovery goals for endangered species had higher ambition than threatened species under ESA and SARA, and; (4) no recovery goal aimed to restore populations to historic levels. Combined, these findings provide guidance to strengthen recovery goals and improve subsequent conservation outcomes. In particular, species at risk planners should seek to attain higher recovery goal ambition, particularly for SARA-listed species, and include quantitative recovery goals wherever possible.
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".