Burbot: Ecology, Management, and Culture
Bibliographic record
Abstract
Abstract.—Movements of 11 sonic tagged burbot Lota lota were examined in the Kootenai River, Idaho, USA and Kootenay River and Kootenay Lake, British Columbia, Canada through up to three spawning seasons. Our objectives were to determine seasonal movements, differences in behavior of individual burbot, and the role of temperature and discharge on prespawn movement. Burbot demonstrated multiple movement patterns: 3 burbot were very mobile, 3 appeared to be intermediate in activity, and 5 were sedentary in summer, while 2 of the 11 entered Kootenay Lake and returned to the river. Most burbot began in autumn what may have been prespawn migrations when river temperatures fell to a range of 3.0–4.9°C. Six burbot entered the Goat River during the spawning season, of which five showed a multiple-year pattern of fidelity, and four returned to an apparent home pool and then exhibited sedentary behavior until the following winter. Three of the 11 burbot demonstrated an apparent nonspawning or rest year, but this was thought to be habitat-related. Logistic regression analysis of three of the six fish entering the Goat River suggested their migration to be best correlated to decreasing temperature and discharge. If the logistic model were representative of the population, then predicted migrations of burbot to the Goat River during winter would have followed a consistent pattern in November preLibby Dam, while postLibby Dam showed migrations to be unpredictable. Results of this study suggest that burbot had multiple life history patterns and several spawning locations and that rehabilitation measures should promote cooler winter water temperatures less than 5°C and discharges less than 300 m3/s.
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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 | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".