A global systematic review of lead exposure and its health effects in wild mammals
Bibliographic record
Abstract
Lead (Pb) is a toxic non-essential metal, known mainly for causing poisoning of humans and wild birds.However, little is known about Pb exposure and its associated health effects in wild mammals.We conducted a global systematic literature review to identify peerreviewed studies published on Pb exposure in wild mammalian species and the health effects they identified.In total, 183 studies, conducted in 35 countries and published over 62 yr , were included in the review.Only 6% (11/183) of the studies were conducted in developing countries.Although 153 mammalian species were studied, most studies focused on species that are easy to access (i.e., hunted species and small mammals that are easy to trap).Therefore, carnivores and scavengers were less frequently studied than herbivores and omnivores.Despite all studies reporting Pb concentrations, only 45 (25%) studies investigated health effects and, of these 45 studies, only 28 (62%) found any health effect in 57 species.All health effects were negative, and ranged from subclinical effects to fatality.Methodologies of Pb sampling and quantification, and reporting of results, varied widely across the studies, making both Pb concentrations and health effects difficult to compare and evaluate.Thus, there is a need for more research on Pb exposure and its health effects on wild mammals, especially as carnivores and scavengers could be used as sentinels for ecosystem health.
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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 | 0.011 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.009 |
| Bibliometrics | 0.013 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".