Bibliographic record
Abstract
Selenium is increasingly an issue for a wide range of mining, industrial, and agricultural operations. Appropriate methods for evaluating the impacts of selenium in aquatic ecosystems are vigorously debated in the literature. Two common approaches include the use of tissue residue guidelines and reproductive toxicity testing using field-collected fish; however, each approach on its own does not provide sufficient evidence that wild fish populations are in fact impaired. The limitations of each method are discussed, and recommendations to improve the relevance of each line of evidence are provided. A 3rd line of evidence, field measurement of fish population dynamics, is proposed and also discussed. A framework, consistent with an ecological risk assessment methodology, for the design, application, and interpretation of selenium weight-of-evidence investigations is proposed.
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 | 0.235 | 0.479 |
| Meta-epidemiology (narrow) | 0.006 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.014 |
| Bibliometrics | 0.050 | 0.016 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.011 | 0.007 |
| Research integrity | 0.014 | 0.011 |
| Insufficient payload (model declined to judge) | 0.008 | 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".