How lipid content and temperature affect American shad (<i>Alosa sapidissima</i>) attempt rate and sprint swimming: implications for overcoming migration barriers
Bibliographic record
Abstract
How seasonal effects such as temperature increases and reduced lipid content affect the ability of anadromous fishes to traverse high-velocity barriers and sprint swimming is poorly understood. We evaluated American shad (Alosa sapidissima) swimming performance in a flume against high flow velocities (2.5–3.7 m·s−1) during the upstream migration period (April–May; temperatures 11.1–21.4 °C) to determine how their willingness to enter a velocity barrier (attempt rate) and their swimming endurance changed during migration. American shad did not make attempts at low temperatures, and attempt rate gradually increased throughout the migration as temperatures warmed. American shad displayed two distinct, nonsustained swimming modes (prolonged and sprint swimming), and endurance was different between sexes. At warmer temperatures, females swam at prolonged speeds more often and longer females displayed a lower endurance. Males primarily swam at sprint speeds and were affected by swimming speed, fork length, and lipid content. Our results indicate that American shad motivation and swimming endurance change over the course of the migration as conditions change, potentially limiting their ability to pass barriers.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".