Surface sediment elemental compositions of 167 Canadian lakes show widespread exceedance of quality guidelines for metals
Bibliographic record
Abstract
Sediments form a key part of lake ecosystems and play important roles in biological and chemical processes. Yet in the most lake-rich country in the world, Canada, there was no standardized portrait of lake sediment elemental compositions and knowledge was lacking about how frequently field data exceeded sediment quality guidelines. To address these gaps and generate a more comprehensive understanding of large-scale spatial patterns in surface sediment geochemistry, we undertook an analysis of 167 lakes sampled by the NSERC Canadian Lake Pulse Network. We analyzed sediment elemental compositions and identified three geographic regions with distinct sediment geochemistry by applying a cascade multivariate regression tree analysis (cMRT). Of these regions, sediments in eastern Canada had relatively high concentrations of metals, while central Canada and southwestern Ontario lakes had relatively high concentrations of detrital elements. Urbanization was correlated with elevated sediment metal concentrations whereas agricultural and pastoral activities were correlated with elevated concentrations of detrital elements. Comparisons between sites with low and high levels of anthropogenic land use indicated limited differences in sediment elemental compositions. However, 70 % of all sites exceeded the guidelines for at least one of the six potentially toxic elements with published sediment quality guidelines that we examined. Since these guidelines were designed to be conservative, we recommend the development of regional sediment quality guidelines for implementation across Canada. • We conducted the first pan-Canadian study of lake surface sediment geochemistry. • Eastern Canadian sediments had higher metal concentrations relative to the rest of Canada. • There were limited differences in geochemistry between sites with relatively low vs. high watershed disturbance. • Concentrations of potentially toxic elements often exceeded sediment quality guidelines. • Regional guidelines for potentially toxic elements are needed.
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 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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".