Habitat characteristics affecting occurrence of a fluvial species in a watershed altered by a large reservoir
Bibliographic record
Abstract
Abstract Flooding river valleys following construction of dams restrict fluvial environments to reaches that were formerly headwaters. Whether remaining habitat is suitable for all life stages of fluvial species is poorly understood. A fluvial species, Arctic grayling Thymallus arcticus, showed a dramatic decline following flooding of the Upper Peace River and the formation of the Williston Reservoir. We related landscape and field site‐specific features with occurrence of juvenile Arctic grayling using an information theoretic approach. For the landscape model, an association was identified between stream order and Arctic grayling occurrence although stream order alone was a poor predictor. A positive association between juvenile Arctic grayling occurrence and distance from the Williston reservoir and stream order, as well as a negative association with water temperature and temperature variance, was deemed important for the field site model. Both modelling approaches indicated size of stream system to be an important influence on occurrence of juvenile grayling in the Williston watershed. River length required for suitable river habitat for salmonids has not previously been identified, but should be factored into future management plans when evaluating the impact of proposed hydroelectric dams and subsequent flooding of river systems.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".