Development of a bioaccumulation model for selenium oxyanions and organoselenium in stream biota
Bibliographic record
Abstract
Selenium (Se) occurs in natural surface waters as a variety of inorganic and organic chemical species, typically dominated by the oxyanions selenate and selenite. Organoselenium species, although hypothesized to be more bioavailable than oxyanions, have rarely been identified or quantified in natural waters and little is known about their fate or bioaccumulative potential. We studied spatial patterns of bioaccumulation in relation to aqueous Se speciation over 5 years at more than 100 locations near coal mine operations in southeast British Columbia, Canada. We used a sequential approach to fitting bioaccumulation model coefficients, first using sites with no detectable organic Se species (< 0.01 µg L-1) to describe the bioaccumulation of selenate and selenite, then applying those relationships to the remaining sites to infer the bioavailability of detectable organoselenium species. Our analysis indicated that the methylated species methylseleninic acid was the most bioaccumulative form, followed by dimethylselenoxide. Organoselenium species were associated primarily with mine sedimentation ponds and are presumed to be products of Se metabolism by algae and bacteria. Highly bioavailable organoselenium species exported from the ponds appear to be responsible for enhanced Se bioaccumulation in biota in downstream lotic reaches, with this influence diminishing with distance from ponds as concentrations decline. Our findings indicate that managing biological productivity in mine sedimentation ponds could help manage Se risk in the receiving environment.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".