The effect of substratum type on aspects of swimming performance and behaviour in shortnose sturgeon<i>Acipenser brevirostrum</i>
Bibliographic record
Abstract
The swimming performance and associated swimming behaviour (i.e.substratum‐skimming, station‐holding and free swimming) were assessed in shortnose sturgeonAcipenser brevirostrumduring critical swimming and endurance swimming tests over a rough and a smooth substratum. It was hypothesized that the addition of a rough substratum in the swimming flume may provide a surface for theA. brevirostrumto grip and offer an energetic advantage. Substratum type did not affect the critical swimming performance, butA. brevirostrumconsistently performed more bottom behaviours (i.e.substratum‐skimming and station‐holding) while on a smooth substratum.Acipenser brevirostrumhad little contact with the rough substratum until the velocity was >1 body length s−1. Endurance swimming time was significantly lower forA. brevirostrumover the rough bottom at the highest velocity (30 cm s−1) which may be attributed to the observed increase in free swimming and decrease in bottom behaviours. During endurance swimming, the rough substratum was mainly used at intermediate velocities, suggesting that there may be a stability cost associated with being in contact with the rough substratum at certain velocities.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".