A Comparison of Egg Funnel and Egg Bag Estimates of Egg Deposition in Grand Traverse Bay, Lake Michigan
Bibliographic record
Abstract
Abstract Reefs are used for spawning by Great Lakes fishes such as the lake trout Salvelinus namaycush, lake whitefish Coregonus clupeaformis, and cisco C. artedi. We designed egg funnels as a new type of sampling gear for quantifying egg deposition while minimizing losses of eggs to physical disturbance and predation. Egg funnels were compared with traditionally used egg bags to quantify egg deposition by spawning fish. In addition, the efficiencies of the egg funnels and egg bags were measured by seeding each gear type with artificial lake trout and lake whitefish eggs in situ. Egg funnels were as efficient as or more efficient than egg bags for capturing artificial eggs and naturally deposited lake trout eggs in both 2008 and 2009. Egg funnels had lower efficiency than egg bags for capture of natural coregonid eggs in 2008, potentially due to avoidance of the gear by spawners; gear avoidance was reduced by burying the egg funnels more deeply in 2009. Accurate estimates of egg deposition will assist fisheries managers in making more-informed decisions for management actions, such as stocking strategies and habitat protection. Received November 29, 2010; accepted April 26, 2011
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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 | 0.001 | 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".