Lentic, lotic, and sulfate-dependent waterborne selenium screening guidelines for freshwater systems
Bibliographic record
Abstract
There is consensus that fish are the most sensitive aquatic organisms to selenium (Se) and that Se concentrations in fish tissue are the most reliable indicators of potential toxicity. Differences in Se speciation, biological productivity, Se concentration, and parameters that affect Se bioavailability (e.g., sulfate) may influence the relationship between Se concentrations in water and fish tissue. It is desirable to identify environmentally protective waterborne Se guidelines that, if not exceeded, reduce the need to directly measure Se concentrations in fish tissue. Three factors that should currently be considered in developing waterborne Se screening guidelines are 1) differences between lotic and lentic sites, 2) the influence of exposure concentration on Se partitioning among compartments, and 3) the influence of sulfate on selenate bioavailability. Colocated data sets of Se concentrations in 1) water and particulates, 2) particulates and invertebrates, and 3) invertebrates and fish tissue were compiled; and a quantile regression approach was used to derive waterborne Se screening guidelines. Use of a regression-based approach for describing relationships in Se concentrations between compartments reduces uncertainty associated with selection of partitioning factors that are generally not constant over ranges of exposure concentrations. Waterborne Se screening guidelines of 6.5 and 3.0 μg/L for lotic and lentic water bodies were derived, and a sulfate-based waterborne Se guideline equation for selenate-dominated lotic waters was also developed. Environ Toxicol Chem 2017;36:2503-2513. © 2017 SETAC.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".