Rehabilitation Needs for Burbot in the Kootenai River, Idaho, USA, and British Columbia, Canada
Bibliographic record
Abstract
Abstract We developed rehabilitation goals for burbot Lota lota in the Kootenai River, Idaho and British Columbia. After developing a catchability model for burbot in the Kootenai River, we used capture rates in two Alaskan rivers to develop surrogate rehabilitation targets for burbot in the Kootenai River. Then we used demographic statistics for burbot in the Kootenai River in a stochastic density-dependent population model to estimate recruitment rates for population rehabilitation. If recruitment failure continued, the population would decline to extinction by 2018. However, the population would reach the interim target abundance of 5,500 individuals (45 fish/km; 3.0 fish/ha) within 25 years if each adult produced 0.85 recruits per year, and it would reach the ultimate target abundance of 17,500 individuals (143 fish/km; 9.6 fish/ha) if each adult produced 1.1 recruits per year. After 25 years, 5,500 burbot would be present with 50% likelihood at a recruitment rate of 0.85 recruits per adult and 95% likelihood at a recruitment rate of 0.88 recruits per adult; 17,000 burbot would be present with 50% likelihood at a recruitment rate of 1.03 recruits per adult and 95% likelihood at a recruitment rate of 1.16 recruits per adult. The time to reach the interim rehabilitation goal of 5,500 burbot would decline from 50 to 25 years as the recruitment rate increased from 0.72 to 0.87 recruits per adult; the time to reach the ultimate rehabilitation goal of 17,500 burbot would decline from 50 to 25 years as recruitment increased from 0.82 to 1.07 recruits per adult. We recommend the following rehabilitation goals for burbot in the Kootenai River: (1) an interim population goal of 5,500 burbot (0.484 fish per net-day) and an ultimate population goal of 17,500 burbot (1.23 fish per net-day); (2) population rehabilitation within 25 years; and (3) an annual recruitment rate of 0.85–1.1 recruits per adult.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".