Threats of environmental mercury to birds: knowledge gaps and priorities for future research
Bibliographic record
Abstract
Summary Anthropogenic emissions of mercury have doubled over the past two centuries. Mercury is a dangerous neurotoxin that threatens human health and fish and wildlife populations. The effects of mercury on birds have been relatively well-studied in the laboratory and in nature. Several aspects of neurology, physiology, behaviour, and reproduction have been shown to be adversely affected. Many studies have documented ataxia, lethargy, reduced appetite, reduced egg production, poor hatching success, and aberrant parental care in birds exposed to mercury. The majority of the research done to date, however, has been focused on select taxa (waterbirds), trophic levels (piscivores), habitat types (aquatic systems), geographic regions (North America and Europe), and life history stages (reproduction), leaving the assessment of mercury's threats to birds incomplete. Successful bird conservation strategies are dependent on a comprehensive understanding of the threats facing populations. Here, I discuss the significant knowledge gaps that remain and subsequently suggest priorities for future mercury research in birds. Studies of mercury in terrestrial, insectivorous, and/or passerine species, and how mercury affects migration are especially recommended to fill gaps in our present understanding.
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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 | 0.010 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.013 | 0.001 |
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".