Bibliographic record
Abstract
Abstract Sediment from a wetland receiving runoff from a coal mine waste dump in the Elk River Valley of southeast British Columbia was assessed for potential selenium uptake. Selenite [SeO32-, Se(IV)] was found to adsorb to the washed sediment at pH 7 to 8, whereas no selenate [SeO42-, Se(VI)] was adsorbed, in the concentration range of 8 to 225 μg L-1 Se as selenite or selenate. Sulfate- and selenate-reducing bacterial activity was detected in the sediment. In the presence of sulfate-reducing bacteria growth medium, Se as selenate was reduced from 619(±53) μg L-1 to 15(±0.7) μg L-1, and in the presence of selenate-reducing bacteria growth medium, Se as selenate was reduced from 364(±66) mg L-1 to 22(±10) mg L-1. Semi-continuous microcosms containing sediment overlaid with selenate (500 μg L-1 Se) and sulfate (0.9 g L-1) containing water were amended with plant debris from the site or nutrients (lactate and fertilizer). Potential selenate reduction rate (0.76 h-1) was highest in the unamended microcosms. Amendment with plant debris from the site had a negative effect on selenate reduction rate in the short term (after one hour) and a positive effect on Se removal in the long term (after one week). This study suggests that wetland sediments at the mine site may be important sinks for Se.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| 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".