A dam passage performance standard model for American shad
Bibliographic record
Abstract
Objectives for recovery of alosines commonly involve improving fish passage at dams during migration. However, a quantitative basis for dam passage performance standards is largely absent. We describe development of a stochastic life-history-based simulation model for American shad, Alosa sapidissima, to estimate effects of dam passage and migratory delay on abundance, spatial distribution of spawning adults, and demographic structuring in space and time. We used the Penobscot River, Maine, USA, as a case study to examine sensitivity of modeled population metrics and probability of achieving specific management goals to inputs. Spawner abundance and percentage of repeat spawners were most sensitive to survival and migration delay at dams, marine survival, and temperature cues for migratory events. Recovery objectives related to abundance and spatial distribution of spawners were achievable under multiple scenarios, but high rates of upstream and downstream passage were necessary. The simulation indicated trade-offs between upstream and downstream passage efficacy whereby increased downstream passage was required to maintain or increase population abundance in conjunction with increased upstream passage. This model provides a quantitative support tool for managers to inform ecologically based decisions about a suite of management scenarios to facilitate recovery and sustainability of diadromous fish populations.
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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 | 0.002 |
| 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 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".