In situ behavioral responses of crustacean zooplankton to an approaching seismic survey
Bibliographic record
Abstract
Abstract The impacts of underwater noise from seismic surveys on zooplankton remain poorly understood despite their critical ecological role. This study investigated the effects of in situ airgun shots on the swimming behavior of the copepod Calanus finmarchicus at distances from over 4000 to less than 100 m from the seismic airgun array (3060 in3, 50.1 L). Copepods were deployed in a cage equipped with a stereo camera system to track individual swimming behavior. Our findings reveal significant changes in swimming speeds and speed-based behavioral classifications: Swimming, Sinking, and Jumping. During airgun exposure, the swimming speed increased significantly, displaying a quadratic relationship around an airgun shot. More copepods jumped, with higher relative jumping counts per individual, following a non-linear relationship with distance from the seismic source. Sinking duration decreased, while swimming lasted longer during shoot periods. Furthermore, our findings suggest that changes in fluid flow speeds and low-frequency sound induced by airgun shots may have driven some of the observed responses, underscoring the complex interaction between seismic activity and copepod behavior. This study not only sheds light on the behavioral effects of impulsive noise on pelagic copepods but also introduces a novel methodology for field research involving small aquatic organisms.
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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".