Fast-start swimming performance and reduction in lateral plate number in threespine stickleback
Bibliographic record
Abstract
Threespine stickleback (Gasterosteus aculeatus) have colonized freshwater habitats in circumboreal coastal regions, resulting in populations with variable but generally reduced lateral plate numbers compared with marine ancestors. Several abiotic and ecological factors associated with variation in lateral plate number among freshwater populations of G. aculeatus have been found, including large-scale climatic effects, variation in water-flow rates and levels of dissolved calcium, and the presence or absence of predatory fish. In addition, it has been proposed that plate reduction might be an adaptation for evading predator pursuit that enhances fast-start performance. If this hypothesis is correct, one would predict that fast-start performance would improve as lateral plate numbers decrease. I tested this prediction by comparing fast-start performance among stickleback with different numbers of lateral plates within two freshwater populations. Fast-starts of individual stickleback were video-recorded at 60 Hz and maximum velocity, maximum acceleration, displacement, and body curvature were calculated for each fish. Lateral plate number was significantly negatively correlated with velocity and displacement but not with acceleration or curvature. These results suggest that reduction in lateral plate number has the potential to be advantageous in some predation regimes because of its association with enhanced fast-start 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".