Selenium Speciation in Whole Sediment using X-ray Absorption Spectroscopy and Micro X-ray Fluorescence Imaging
Bibliographic record
Abstract
A field survey was conducted in a freshwater lake system in the Athabasca Basin, northern Saskatchewan, Canada that receives treated metal mining and milling process effluent containing elevated levels of selenium. Whole sediment, pore water, surface water, and chironomid larvae were analyzed in an attempt to link whole sediment selenium speciation to various environmental factors, including selenium availability to benthic macro-invertebrates, a trophic level through which selenium can enter the diet of higher trophic level organisms. Speciation was measured using synchrotron-based selenium K-edge X-ray absorption spectroscopy (XAS). All lake averages of sediment samples (reference or exposure sites) contained a significant proportion (approximately 50%) of elemental selenium which is relatively insoluble in water, immobile, and not considered to be bioavailable. The presence of elemental selenium was confirmed by extended X-ray absorption fine structure (EXAFS) analysis of select samples. Inorganic metal selenides were also found in whole sediment samples and confirmed using micro X-ray fluorescence imaging. Dissolved selenium concentrations in pore water were correlated to the amount of selenite in whole sediments provided that the sites were classified according to whole sediment sand content. Sand content itself is likely inversely correlated to sediment organic matter content, adsorption sites, and redox potential.
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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 | 0.001 |
| 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) | 0.001 | 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".