Drought-induced metal release from a wetland at Plastic Lake, central Ontario
Bibliographic record
Abstract
With climate change, droughts may become more frequent in southern Ontario, which could release metals from peat and degrade downstream water quality. Monthly volume-weighted metal (Al, Ba, Be, Cd, Co, Mn, Ni, Pb, Sr, and Zn) concentrations and fluxes in streams and bulk deposition at Plastic Lake were monitored over 20 months in 2002–2003, during which there was a summer drought. Monthly concentrations in the outflow from the wetland (PC1) were variable, with very high concentrations following the drought. With the exception of Pb, statistically significant models of metal concentrations with SO42– and dissolved organic carbon concentrations were developed, and these relationships were used to estimate monthly metal exports between 1980 and 2000. Model predictions for Cd and Zn in PC1 agreed well (p < 0.001) with concentrations measured between 1989 and 1991. Model predictions suggesting peaks in metal concentrations are common in years with pronounced summer droughts. In contrast to ombrotrophic bogs, the PC1 wetland receives the majority of its metal input from the terrestrial catchment, and mass balance approximations indicate no substantial depletion of metal reserves in peat. Drought-induced metal peaks may persist for many decades, potentially contributing to the delayed recovery of surface waters at Plastic Lake, despite declining S deposition.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".