Influence of temperature on selenium mobility under contrasting redox conditions: a sediment flow-through reactor experiment
Bibliographic record
Abstract
We studied selenium (Se) sequestration in minimally disturbed lacustrine sediments using flow-through reactors (FTR) in response to organic matter lability, selenium (Se) speciation and temperature (4 and 23°C). Initial sediment was composed of either fresh or aged organic matter (OM), and was fed with environmentally relevant, low Se concentrations and filtered lake water. We monitored Se concentration as well as speciation along with pH and the concentrations of dissolved OM, NO3-, NO2-, Fe(II), SO42- and HS- in the outflow of FTRs during 8 experimental phases along increasing Se concentrations. All experiments sequestered a large proportion of Se. Fresh, labile OM removed 50% more Se than aged, more recalcitrant OM. Along with a highest proportion of reduced redox-sensitive species in the reactors with fresh OM, this result is consistent with reducing conditions promoting Se sequestration. Inflowing selenite was sequestered to a larger extent than inflowing selenate. Lastly, only selenate reduction responded strongly to temperature. At 100 nM inflow, selenate was sequestered at a rate of 92 pmol cm-3 d-1 at 23°C, which lowered to 80 pmol cm-3 d-1 at 4°C. Outflow Se speciation for selenate reduction experiments comprised mostly of organic Se species at 23°C and, in contrast, solely of selenate at 4°C. We hypothesize that selenate reduction proceeded via microbial processes, in line with reactions catalyzed by enzymes being temperature dependent. Overall, our findings suggest that the mobilisation and warming of the boreal and permafrost carbon pools may increase the capacity of aquatic environments to sequester Se, lowering its bioavailability.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".