Biology, distribution and management of burbot (Lota lota) in Washington State
Bibliographic record
Abstract
There are recent concerns that burbot stocks have been declining in some Northwestern states and Canadian Provinces. Therefore, we investigated the distribution, status and management history of burbot stocks in Washington, and compared their growth, condition, and life history characteristics with those in other regions. Eleven stocks of burbot occurred in eastern Washington, primarily in large, deep lakes and reservoirs of the upper Columbia and Yakima River watersheds. Status of three stocks was known: the Lake Roosevelt burbot stock has increased; the Palmer Lake stock has declined; and the non-indigenous stock in Banks Lake may be extinct. Average growth rate of age 1-10 burbot from four Washington lakes was slower than that in Midwestern states, but similar to that in Alaska, Northern Canada, and Wyoming. Washington burbot over age 10 grew at slower rates than those in all other regions. Average relative weight of Washington burbot was similar to that in reservoirs in other areas of the country, but less than that of lake populations in those other areas. We reported available harvest rates of Washington burbot, but there was insufficient information to determine what impact angler harvest has on most Washington populations
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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".