Paleo-ecotoxicology: What Can Lake Sediments Tell Us about Ecosystem Responses to Environmental Pollutants?
Bibliographic record
Abstract
The development of effective risk reduction strategies for aquatic pollutants requires a comprehensive understanding of toxic impacts on ecosystems. Classical toxicological studies are effective for characterizing pollutant impacts on biota in a controlled, simplified environment. Nonetheless, it is well-acknowledged that predictions based on the results of these studies must be tested over the long-term in a natural ecosystem setting to account for increased complexity and multiple stressors. Paleolimnology (the study of lake sediment cores to reconstruct environmental change) can address many key knowledge gaps. When used as part of a weight-of-evidence framework with more traditional approaches in ecotoxicology, it can facilitate rapid advances in our understanding of the chronic effects of pollutants on ecosystems in an environmentally realistic, multistressor context. Paleolimnology played a central role in the Acid Rain debates, as it was instrumental in demonstrating industrial emissions caused acidification of lakes and associated ecosystem-wide impacts. "Resurrection Ecology" (hatching dormant resting eggs deposited in the past) records evolutionary responses of populations to chronic pollutant exposure. With recent technological advances (e.g., geochemistry, genomic approaches), combined with an emerging paleo-ecotoxicological framework that leverages strengths across multiple disciplines, paleolimnology will continue to provide valuable insights into the most pressing questions in ecotoxicology.
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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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.005 | 0.009 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".