Noisy waters affect feeding and metabolic enzyme activities of amphipod mesograzers
Bibliographic record
Abstract
The consequences of the progressive intrusion of anthropogenic noise into natural oceanic soundscapes are largely unknown despite the fact that most metazoans utilize sound or vibration for intra- and interspecific interactions as well as orientation. Coastal ecosystems in particular can be characterized by a high level of human activity and thus anthropogenic noise sources. Here, we studied the impact of such noise on marine mesograzers, small invertebrates that play crucial ecological roles by controlling macrophyte growth while serving as key food sources for higher trophic levels in algae-dominated coastal ecosystems. The effect of added continuous low-frequency noise on the habitat choice, feeding rate, and metabolic enzyme activities of the amphipod mesograzer Marinogammarus marinus was tested in a controlled laboratory setting. While habitat choice appeared unaffected by the added noise, exposed animals exhibited reduced feeding performance as well as altered metabolic enzyme activities. If sustained, the physiological homeostasis of M. marinus and their role in the ecosystem may be jeopardized. The ever-rising sound levels in the oceans may thus have consequences for algae-dominated animal communities in coastal areas. • Added low-frequency noise (LFN) affects amphipod mesograzer physiology and behavior • Feeding performance of Marinogammarus marinus was reduced when exposed to LFN • Activities of metabolic enzymes were altered after a 5-day exposure to LFN • Habitat choice appeared to be unaffected by the added noise
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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".