Less specific recovery strategy targets for threatened and non-charismatic species at risk in Canada
Bibliographic record
Abstract
Abstract Recovery strategies for species at risk have been criticized for a lack of specificity (i.e. measurable and quantifiable criteria) as well as for taxonomic biases, both of which may ultimately affect species’ recovery. However, it is unknown whether the clarity and specificity of written statements within recovery strategies can also influence recovery efforts for certain species at risk. To assess this we examined the variation in semantic uncertainty in the target statements of recovery strategies for Canadian species at risk at the federal and provincial levels. We quantified the lexical density and readability of recovery target statements and examined them for differences according to taxonomic grouping, jurisdiction and degree of endangerment. Recovery statements for the category threatened species had greater semantic uncertainty than those for higher (endangered) and lower (special concern) categories, which is likely to be a function of the fact that threatened species are less abundant than special concern species but are subject to greater errors in population estimates than endangered species. We also found that recovery statements for non-charismatic species (e.g. plants and invertebrates) had greater semantic uncertainty than those for other taxa, which may be related to the resources available for studying and conserving them. Our results suggest a need for greater specificity in recovery targets for threatened and non-charismatic species, and that more focused data collection on these species’ populations is warranted.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".