Supplementary information from: A systematic review of trace elements in the tissues of bats (Chiroptera)
Bibliographic record
Abstract
Bats constitute about 22% of known mammal species; they have various ecological roles and provide many ecosystem services. Bats suffer from several threats caused by anthropization, including exposure to toxic metals and metalloids. In our systematic review, we analyzed 75 papers to investigate how species, diet, and tissue type impact bioaccumulation in bat species all over the world. Most studies documented element accumulation in fur, liver, and kidney; at least 36 metals and metalloids have been measured in bat tissues, among the most studied were mercury and zinc. Comparisons with known toxicological thresholds for other mammals showed concerning values for mercury and zinc in bat hair, lead and some essential metals in liver, and iron and calcium in kidneys. Moreover, accumulation patterns in tissues differed depending on bat diet: insectivorous bats showed higher metal concentrations in fur than in liver and kidney while frugivorous species showed higher values in liver and kidney than in fur. This review points out several information gaps in the understanding of metal contamination in bats, including a lack of measured toxicity thresholds specific for bat tissues. Data on trace element bioaccumulation and its associated health effects on bats is important for conservation of bat species, many of which are threatened.
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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 | 0.002 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.013 | 0.017 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.160 | 0.015 |
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".