Integrating acoustic telemetry and demographic modeling to inform cisco (<i>Coregonus artedi</i>) restoration in Keuka Lake, New York
Bibliographic record
Abstract
Stocking is an important conservation tool to restore fish populations. Yet, assessing restoration success is often limited by a lack of field-based demographic information at low abundance, particularly for juvenile fish. Using outcomes from cisco ( Coregonus artedi) reintroductions to Keuka Lake, New York, USA, we demonstrate a data-driven approach to assess fish stocking performance and evaluate the likelihood of achieving conservation goals. Multistage juvenile survival estimates from acoustic telemetry quantified high post-stocking mortality rates across three distinct stages. Modeled post-stocking stages include immediate release, acclimation, and long-term survival assumed to reflect natural mortality of cisco in Keuka Lake. High juvenile mortality severely limited the probability that stocked fish will reach reproductive maturity, and population viability analysis with a Leslie matrix life-stage model indicated that re-establishing a cisco population is unlikely with current stocking practices and lake conditions. By contrast, using cisco life history parameters extrapolated from other systems would have resulted in false optimism for restoration success. Our results highlight the importance of utilizing in situ demographic estimates for designing and implementing conservation stocking efforts.
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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 | 0.001 |
| 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 | 0.001 | 0.001 |
| 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".