Swimming performance and behaviour of young-of-the-year shortnose sturgeon (<i>Acipenser brevirostrum</i>) under fixed and increased velocity swimming tests
Bibliographic record
Abstract
Swimming performance and behaviour in fish has been shown to vary depending on the investigation method. In this study, an endurance swimming curve was generated for young-of-the-year shortnose sturgeon (Acipenser brevirostrum LeSueur, 1818) (~7 cm total length, ~2 g) and compared with values determined in a separate incremental swimming (critical swimming, Ucrit) test. Using video, tail-beat frequency (TBF) was quantified and compared for fish swimming under both swimming tests. From the endurance-curve analysis, it was found that sturgeon did not display a statistically significant burst swimming phase. Maximum sustainable swimming speed (calculated to be 18.00 cm·s–1) from the endurance curve occurred at ~80% of Ucrit(22.30 cm·s–1). TBF was similar at all speeds for both swimming tests, except at speeds approaching Ucrit, where fish displayed TBFs of 4.29 Hz for the endurance protocol and 2.26 Hz for the Ucritprotocol. TBF was more variable between individuals swimming at the same speed within the Ucritcompared with the endurance protocol. Finally, a significant negative correlation was found between TBF and Ucritin individual fish, suggesting that station-holding may be an important energy saving strategy during swimming in this size class of sturgeon.
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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 | 0.000 |
| 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".