Swimming performance of blacknose dace (<i>Rhinichthys atratulus</i>) mirrors home-stream current velocity
Bibliographic record
Abstract
Flowing waters may represent a force that structures the locomotor capacity of stream fishes. We used a modified critical swimming speed (U crit ) procedure to investigate the relationship between base-flow conditions and locomotor performance of blacknose dace (Rhinichthys atratulus) from five sites within three watersheds of Baltimore County, Maryland. Our modified test used 5-min intervals between incremental increases of 5 cm·s 1 in swim-tunnel current velocity. This time increment represented a realistic transit time across riffles found in the home streams of dace. To characterize current velocity conditions of the streams, we measured current velocity at 55 evenly spaced points per site during base-flow conditions. Swimming performance varied greatly among 32 individual fish from the five sites ( 5 5 U crit from 26.33 to 69.00 cm·s 1 ) and was positively correlated (r 2 = 0.38, p = 0.002) with mean base-flow current velocities at the site of collection. Additionally, among fish from the site with the widest and most even distribution of current velocities (from 0 to 54 cm·s 1 ), we observed the largest range of swimming performances. Our results suggest that variation in flow conditions among streams influences swimming ability of blacknose dace and can result in heretofore-unappreciated intraspecific variation in swimming performance.
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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".