Estimates of egg deposition and effects of lake trout (<i>Salvelinus namaycush</i>) egg predators in three regions of the Great Lakes
Bibliographic record
Abstract
Excessive mortality from spawning through the first year of life is likely responsible for recruitment failures in many lake trout (Salvelinus namaycush) populations. We evaluated relationships between interstitial predator species and lake trout egg deposition in three regions of the Great Lakes. The introduced crayfish Orconectes rusticus dominated the nearshore zone in northern Lake Michigan, whereas other sites in Lake Michigan and Parry Sound were dominated by native Orconectes propinquus. In Lake Champlain, sculpin (Cottus spp.) were the most common interstitial predator, yet densities were comparable with those in Lake Michigan. Predator densities in Lake Michigan (mean ± standard error = 15.5 ± 0.4 predators·m2) were twice those in Lake Champlain or Parry Sound (6 ± 1 predators·m2). Densities of eggs were higher in Lake Champlain (2994.1 ± 398.3 eggs·m2) and Parry Sound (454.7 ± 36.3) than in Lake Michigan (7.5 ± 1.3; Kruskal-Wallis, P ≤ 0.05). Ratios of eggs to predator provide simple measures of the effectiveness of lake trout rehabilitation efforts. The average ratio calculated from 10 successful spawning locations is 190 eggs·predator1·m2. Ratios may help establish target egg densities for restoration efforts and the concentration of spawning adults required to allow survival to the fry stage.
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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 | 0.001 |
| 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) | 0.000 | 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".