The mobility of arsenic in a Canadian freshwater system receiving gold mine effluents
Bibliographic record
Abstract
The mobility of arsenic in freshwater systems is dictated by its partitioning between the solid and dissolved fractions in the sediments and their interstitial waters. Arsenic is largely associated with ferric iron in the form of oxyhydroxides in oxic waters and sediments as arsenate As(V). In deeper anaerobic sediments arsenic is released from iron oxyhydroxides because of the reduction of iron from the ferric to the more soluble ferrous state. Reducing environments can also be encountered in sediments relatively close to the sediment-water interface when there are high rates of biological activity that consume oxygen and create a reducing environment. For the past several years we have examined the relationships between organic carbon content of surface sediments, bottom water anoxia, redox zonation of sediments and the release of arsenic from freshwater sediments to the overlying waters. These studies have been performed using limnocorrals to isolate columns of water and their underlying sediments in Balmer Lake, a shallow freshwater system in Central Canada that has served as the final repository for tailings from two gold mines for more than 40 years. The results indicate that surface sediments with higher organic carbon content are more susceptible to developing late season bottom water anoxia that can facilitate the subsequent release of arsenic from sediments to the overlying water. These results have implications for metal mining operations where reduced metal loadings from effluents or mine closure are expected to result in higher biological productivity and greater organic matter deposition to sediments.
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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 | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".