Comparing water-level policies in a boreal reservoir: How wave and ice energy can help maintain walleye spawning habitat
Bibliographic record
Abstract
Papenfuss JT, Cross T, Venturelli PA. 2017. Comparing water-level policies in a boreal reservoir: how wave and ice energy can help maintain walleye spawning habitat. Lake Reserve Manage. 00:00–00. Water levels in reservoirs affect the timing and depth of wave and ice forces that help maintain walleye (Sander vitreus) spawning habitat. We studied how changes in a water-level management policy (“rule curve”) in 2000 affected these forces on 3 lakes of the Namakan Reservoir, a large boreal reservoir on the border of Canada and the United States. The 2000 rule curve increased mean water levels (0.1 m) during open-water seasons and caused a significant increase in the amount of time each year that wave energy over suitable spawning substrates suspended <0.2 mm sediments at known spawning locations (6–18%, P < 0.01). Conversely, a decrease in the mean range (0.7 m) of water-level elevations during winter seasons caused an 11% decrease (P < 0.01) in the interaction of ice with spawning substrates at known spawning locations. However, ice scour still affected those substrate elevations that were used frequently by walleye during typical spawning seasons. Our findings suggest that water-level management is important for maintaining suitable walleye spawning habitats, and that policies can be designed to optimize those habitat conditions.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".