Burbot: Ecology, Management, and Culture
Bibliographic record
Abstract
Abstract.—We examined the relative abundance of zooplankton populations and burbot Lota lota in six oligotrophic lakes and one river in British Columbia, Canada and the Kootenai River, Idaho, USA. Burbot were primarily sampled November through March, whereas zooplankton were sampled in May to coincide with larval burbot early growth. The highest zooplankton densities in the 2003 and 2004 sampling were in the Columbia, Moyie, and Trout lakes, while Columbia, Kootenay, and Moyie lakes were the highest in biomass. In the 2-year sample period, the highest densities of zooplankton identified in these lakes ranged from 68 to 400/L and biomass ranged from 154 to 1,350/µg/L × 103. Taxonomic breakdown of zooplankton taxa shows that the majority of biomass of Crustacia species sampled was from the subclass Copepoda. When Cladoceran species were present in the sample, however, they made up the majority of the sample in both density and biomass. In all the water bodies sampled, rotifers made up the majority of the proportion of total density (60–92% of the total sample). Most water bodies exhibited rotifer: crustacean densities of approximately 1:1–2:1; the Kootenai River had rotifer:crustacean densities of 12:1 for both years sampled. During the sample period (1993– 2005), burbot were captured in each of the water bodies with known or sampled burbot populations. The highest catch per unit effort (CPUE) recorded was found in the Goat River with as high as 12.3 fish/net d. The Kootenai River had the lowest CPUE of burbot at 0.006 fish/net d. We conclude that trends in zooplankton percent composition may exist in these lakes and suggest that managers of burbot culture closely examine these proportions when choosing a location for extensive rearing in order to maximize survival of larvae through critical early life stages.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".