Finlets and the steady swimming performance of <i>Thunnus albacares</i>
Bibliographic record
Abstract
The functional significance of finlets on the steady swimming performance of yellowfin tuna Thunnus albacares was evaluated by measuring the speed and tail‐beat frequency of the fish with and without them. It was hypothesized that if finlets do improve swimming performance, fish without finlets would have to work harder to maintain the same swimming speed as fish with them and that this would be reflected in kinematic differences. Two‐way ANOVA showed significant effects between individuals on speed (d.f. = 5 and 228, P < 0·001) and tail‐beat frequency (d.f. = 5 and 48, P < 0·001), but no significant effects of treatment on speed (d.f. = 1 and 228, P = 0·25) and tail‐beat frequency (d.f. = 1 and 48, P > 0·1). No interaction effects on speed (d.f. = 5 and 228, P > 0·1) and tail‐beat frequency (d.f. = 5 and 48, P > 0·25) were found. This suggested that finlets were unlikely to function as significant drag reduction and thrust enhancing devices in routine steady swimming. Though not statistically significant, small percentage differences between the mean swimming speeds and tail‐beat frequency of the untreated and treated groups (fish with and without finlets respectively) of the order of 0·5% may be meaningful over the life of a fish. Also, finlets may improve performance at high sustained speeds in rapid accelerations and turns.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".