Hydrological and biogeochemical controls governing the speciation and accumulation of selenium in a wetland influenced by mine drainage
Bibliographic record
Abstract
Abstract Controls governing the speciation and accumulation of Se in a 3.7-ha marsh influenced by mine drainage were assessed through examination of water balance, water quality, sediment, and plant tissue components. Over the 8-mo study period (April through November, 2009), mean monthly flows ranged from 1600 to 2300 m3 d−1 (hydraulic retention time of 1–3 d). Total Se concentrations in the marsh outflow were lower than the inflow by 0.4 to 6.2 μg L−1 (mean difference = 3.3 μg L−1), illustrating Se removal. The Se accumulation pathways are illustrated by elevated concentrations of Se in sediments (3–35 mg kg−1 dry wt) as well as in below-ground (2–41 mg kg−1 dry wt; mean = 10 mg kg−1 dry wt) and above-ground (0.8–6.3 mg kg−1 dry wt; mean = 2 mg kg−1 dry wt) emergent plant tissues. Redox stratification in the shallow water column had a marked effect on Se speciation and behavior, illustrating bottom water removal of dissolved selenate in suboxic horizons and increased mobility of dissolved organo-Se. Mass balance data yielded inflow and outflow loading rates for Se of 27 and 23 g d−1, respectively (net accumulation rate of 4 g d−1 or 0.11 mg m2 d−1). The rate of accumulation as calculated from the mass balance agrees with independently measured rates of Se accumulation in sediments for the site (3.6–8.1 g d−1 or 0.10–0.22 mg m−2 d−1). Environ Toxicol Chem 2018;37:1824–1838. © 2018 SETAC
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".