Palaeotoxicity: reconstructing the risk of multiple sedimentary pollutants to freshwater organisms
Bibliographic record
Abstract
'Real-world' contaminant exposure of sediment-dwelling biota is typically long-term, low-level and to multiple pollutants. However, sediment quality guidelines, designed to protect these organisms, relate only to single contaminants. This study uses radiometrically dated sediment cores from 7 English lakes with varying contamination histories to reconstruct temporal changes in likely risk to biota (herein termed 'palaeotoxicity'). The Probable Effects Concentration Quotient (PEC-Q) approach was used to combine sediment concentrations from multiple contaminants (trace metals; PCBs; PBDEs) to determine risk allocated to metals and persistent organic pollutants (POPs) separately as well as combined (PEC-Q Mean-All). Urban-influenced lakes were considerably more contaminated, exceeding PEC-Q thresholds of 0.5 and 2.0 over long durations (some since the nineteenth century). This has been mainly due to metals (principally lead) and by factors of up to 10 for individual metals and by > 2 for PEC-Q Mean-Metals. In 6 out of 7 lakes, considerable reductions in risk associated with trace metals are observed since emissions reductions in the 1970s. However, at all lakes, PEC-Q Mean-POPs has increased sharply since the 1950s and at 5 out of 7 lakes now exceeds PEC-Q Mean-Metals. These organic pollutants are therefore now the dominant driver behind elevated contaminant risk to sediment-dwelling biota and recent temporal trends in PEC-Q Mean-All remain above threshold values as a result. Finally, PEC-Q Mean-All values were compared to standard biological toxicity tests for surface sediments at each site. While chironomid growth and daphniid reproduction were significantly reduced compared to controls at 5 out of 7, and all lakes, respectively, the scale of these reductions showed only limited quantitative agreement with predicted risk.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.014 | 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".