Mercury and cadmium in striped dolphins (Stenella coeruleoalba) stranded along the Southern Tyrrhenian and Western Ionian coasts
Bibliographic record
Abstract
Pollution from heavy metals is becoming a serious and widespread problem due to their persistent and bioaccumulative nature, moreover in the Mediterranean Sea, threatening food safety and the health of humans and marine animals. Cadmium and mercury in particular, are considered two of the most toxic metals to living organisms. Their presence is associated with the contribution of human activity, implying an increased level in the different environmental compartments and the inevitable bioaccumulation in the food chain.In this study, levels of cadmium and mercury were determined in liver, kidney, and muscle tissue of dolphinid specimens of Stenella coeruleoalba stranded in different locations along the coastal areas of the Tyrrhenian and Ionian Sea in Southern Italy, during the period 2015–2018 by Atomic Absorption Spectrophotometry. Data were compared with those reported for other locations along the Mediterranean sea. The correlations between biometric data (body length, weight and gender) and cadmium and mercury concentrations in samples of cetaceans were statistically analysed in order to investigate the risk these contaminants may pose to the delphinids health.Examination of the pattern of contaminants revealed a significantly high distribution for mercury in all the matrices analyzed (liver, kidney and muscle tissue). On the contrary, elevated concentrations of cadmium were found only in liver (range: 0.005 - 8.95 mg/kg w.w.) and kidney (range: 0.005 - 34.1 mg/kg w.w.) due to accumulator role of these organs in long-term exposures.
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 | 0.001 | 0.001 |
| 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) | 0.000 | 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".