A comparison of the effects of water-level policies on the availability of walleye spawning habitat in a boreal reservoir
Bibliographic record
Abstract
Papenfuss JT, Cross T, Venturelli PA. 2018. A comparison of the effects of water-level policies on the availability of walleye spawning habitat in a boreal reservoir. Lake Reserv Manage. 34:321–333. Water levels in reservoirs can affect the quantity and quality of the habitat that is available for spawning fish and their eggs. We studied how a change in the water-level management policy (“rule curve”) in 2000 affected the availability of walleye (Sander vitreus) spawning habitat in 3 lakes (Kabetogama, Namakan, and Sand Point) that make up a large, boreal reservoir between Canada and the United States. According to observed water-level data, available spawning habitat on known Lake Kabetogama spawning sites increased 95% (P < 0.01) with the 2000 rule curve, but did not change on Namakan or Sand Point lake spawning sites. However, when using modeled water-level data to control for confounding weather events, habitat availability at known spawning sites increased significantly (P < 0.01) on all 3 lakes (179%, 92%, and 93%, respectively). Habitat availability improved because the 2000 rule curve increased mean spring water levels by 0.5 m, and water levels rose more slowly (2.2 vs. 3.0 cm/d) during egg incubation. Our findings suggest that, although water-level management on large reservoirs can be a challenge, carefully designed policies can improve walleye spawning habitat conditions and help to achieve fisheries management goals.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".