Factors influencing lead, mercury and other trace element exposure in birds from metal mining areas
Bibliographic record
Abstract
Non-ferrous metal mining is considered one of the largest sources of toxic metal released to the environment and may threaten ecosystems, notably biota. We explored how birds that inhabit non-ferrous metal mining sites are exposed to mercury, lead, and other trace elements by analyzing their feathers and verifying which factors may influence element concentrations in feathers. We sampled a total of 168 birds, representing 26 species, with different feeding habits and migration patterns in a non-polluted reference site and two historical metal mining areas: Almadén, which is considered one of the most heavily mercury-contaminated sites worldwide, and the Sierra Madrona mountains where lead has been mined since ancient times. The quantification of aluminum (Al), arsenic (As), barium (Ba), beryllium (Be), cadmium (Cd), cobalt (Co), chromium (Cr), copper (Cu), iron (Fe), mercury (Hg), magnesium (Mg), manganese (Mn), molybdenum (Mo), nickel (Ni), lead (Pb), selenium (Se), thorium (Th), thallium (Tl), uranium (U), vanadium (V) and zinc (Zn) was performed by inductively coupled plasma mass spectrometry (ICP-MS). Feather analysis revealed contamination by Hg and Pb, in Almadén and Sierra Madrona, respectively. We found that granivorous birds had the lowest feather Hg levels compared to those found in omnivorous, insectivorous, and piscivorous species, whereas feather Pb was about twice as high in granivores and omnivores, than in insectivorous and piscivorous birds. We also found differences among study sites in 13 elements and confirmed the influence of feather age, migratory patterns of the birds, and external deposition of elements, on metal concentrations in the feathers. Our results highlight that despite the cessation of metal mining in the study areas, local avifauna are being exposed to Hg and Pb from abandoned mines and old tailings sites, indicating that appropriate measures are needed to protect biota from overexposure to these toxic metals.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 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 teacher head, 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".