Increased Walleye Egg-to-Larvae Survival following Spawning Habitat Enhancement in a Tributary of Eastern Lake Ontario
Bibliographic record
Abstract
Abstract To mitigate the effects of sedimentation from surrounding land use, a Newbury weir was installed in Kent's Creek (a tributary of eastern Lake Ontario) by the U.S. Fish and Wildlife Service Partners for Fish and Wildlife Program to enhance spawning habitat for Walleye Sander vitreus. By adding angular crushed limestone, the Newbury weir was designed as an engineered spawning riffle that creates a scouring effect to maintain clean spawning substrate for Walleyes. To evaluate the success of the Newbury weir, we compared rates of egg deposition, egg viability, and larval out-migration between the created spawning riffle and a natural spawning bed for two seasons subsequent to its construction. Spawning Walleyes were found to immediately spawn on the created riffle following construction and demonstrated similar patterns of egg deposition and larval out-migration at both sites each year. When data were pooled between years, significantly higher densities of out-migrating larvae were observed at the Newbury weir despite the finding that significantly more eggs were spawned at the natural site. Considering that egg viability was similar between sites, we deduce that the created riffle had a higher egg-to-larvae survival rate. Our results suggest that the Newbury weir successfully created functional spawning habitat promoting out-migrating larval Walleyes. We anticipate that the created riffle will maintain valuable spawning substrate for Walleyes, but sediments deposited from upstream eroding banks and agricultural land use practices could affect its long-term viability. We recommend that future spawning habitat enhancement projects adopt similar approaches within appropriate fluvial geomorphic settings and additionally consider watershed-scale improvements to enhance Walleye spawning tributaries. Received March 17, 2017; accepted May 26, 2017 Published online August 9, 2017
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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".