Recovery Potential Assessment for Lake Sturgeon in Canadian Designatable Units
Bibliographic record
Abstract
Abstract Significant declines in the abundance of lake sturgeon Acipenser fulvescens across most of its North American range have led to abundances less than 10% of estimated minimum sustainable population sizes and, in some locations, less than 1% of historic abundances. These precipitous declines in abundance have resulted in most lake sturgeon populations being considered a conservation concern and have prompted management actions toward recovery. Here we present modeling in support of a recovery potential assessment, using stage-structured matrix models and population viability analysis to quantitatively assess allowable harm, recovery efforts, probabilities of recovery, and recovery time frames. From this assessment, we conclude that lake sturgeon populations are most sensitive to harm on adult survival and that some designatable units are highly sensitive to any level of harm. However, the scope for recovering lake sturgeon by improving adult survival is limited; instead, larger proportional increases in population growth rates can be achieved by focusing recovery efforts on age-0 and juvenile survival. Finally, based on a recovery target of 1,188 spawning females/year for each discrete population and current abundances from 1% to 10% of this target, long-term projections indicate recovery time frames ranging from 20 to over 100 years, depending on the recovery actions implemented.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".