Shipping noise affecting immune responses of European spiny lobster (<i>Palinurus</i> <i>elephas</i>)
Bibliographic record
Abstract
Marine crustaceans are influenced by numerous environmental factors. Environmental stress from pollutants seems to affect their metabolism, growth, moulting, survival, and immune defence. Recently, it has become clear that there is an impact of the global increase in sea noise levels due to shipping traffic on crustacean welfare. Considering the ecological and commercial importance of the European spiny lobster (Palinurus elephas (Fabricius, 1787)) in most parts of the Mediterranean coastal area, in the present study we investigated whether shipping noise pollution contribute to changing the immune parameters of stress in the European spiny lobster. The animals were exposed to a mix of noises produced by different typologies of the boat played back in a tank, and the values of cellular and humeral parameters were evaluated. Total haemocyte count (THC), haemolymphatic protein concentration, phenoloxidase (PO) activity in cell-free haemolymph, and heat shock protein 27 (Hsp27) expression in haemocyte lysate were considered potential biomarkers of stress. THC and PO activity decreased significantly, whereas total protein and Hsp27 expression increased significantly. Overall, the results demonstrate that the stressful conditions investigated in this study affect both cellular and biochemical parameters in the European spiny lobster.
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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".