Physiological condition of the warty venus (Venus verrucosa L. 1758) larvae modulates response to pile driving and drilling underwater sounds
Bibliographic record
Abstract
Noise is now recognized as a new form of pollution in marine coastal habitats. The development of marine renewable energies has introduced new sonorous perturbations, as the wind farm installation requires pile driving and drilling operations producing low frequency sounds at high sound pressure levels. Exponential expansion of offshore wind farms is occurring worldwide, making impact studies, particularly on benthic species highly abundant and diverse in the coastal area used for wind farming, a necessity. As larval recruitment is the basis for establishing a population, we conducted an experimental study to assess the interactive effects of pile driving or drilling sounds and larval rearing temperature on the endobenthic bivalve Venus verrucosa . In ectothermic animals, temperature modifies the organism’s physiology, resulting in performance variability. We hypothesize that temperature modulation could change larval responses to noise and explore the potential interacting effects of temperature and noise. Using two distinct rearing temperatures, physiologically different batches of larvae were produced with contrasting fatty acid content and composition in the neutral and polar lipid fractions. Without defining any absolute audition threshold for the larvae, we demonstrate that the effects of temperature and noise were ontogenic-dependent and modulated larval performance at the peri-metamorphic stage, acting on the metamorphosis dynamic. At the pediveligers stage, a strong interaction between both factors indicated that the response to noise was highly related to the physiological condition of the larvae. Finally, we suggest that underwater noise reduces the compensatory mechanisms established to balance the temperature increase.
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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) | 0.001 | 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".