Light stimulates swimming behavior of larval eastern oysters Crassostrea virginica in turbulent flow
Bibliographic record
Abstract
Planktonic larvae of the eastern oyster (Crassostrea virginica) are able to regulate their vertical position in the water, but the environmental cues responsible for this regulation, particularly in turbulent settings, remain unclear.We quantified swimming responses of late-stage oyster larvae in a grid-stirred turbulence tank to determine how light affects the swimming behavior of larvae over a range of hydrodynamic conditions similar to their natural coastal environments.We used particle image velocimetry and larval tracking to isolate larval swimming from local flow and to quantify three behavioral metrics: vertical swimming direction, proportion of larvae diving, and proportion of larvae swimming helically.We compared these metrics across turbulence levels ranging from still water ( = 0 cm 2 s -3 ) to estuarine-like conditions ( = 0.4 cm 2 s -3 ) in light and dark.In all turbulence levels, light had no effect on the proportion of upward swimming larvae, but elicited detectable increases in the proportion of helical swimming and diving behaviors.We further examined the effect of light and turbulence on specific characteristics of helical trajectories, and found that these environmental cues induce changes to both vertical and horizontal velocities of helically swimming larvae, changing the helix geometry.The increased prevalence of these behaviors in light likely plays an ecological role: increased diving in light (in conjunction with turbulence) is a potential mechanism to enhance settlement success, while changes to helical swimming in light may serve an anti-predatory function.Together, these behaviors provide insight into potentially complex larval responses to multiple simultaneous environmental cues.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".