Do water quality criteria based on nonnative species provide appropriate protection for native species?
Bibliographic record
Abstract
The potential use of toxicity data for nonnative species to derive water quality criteria is controversial because it is sometimes questioned whether criteria based on species from one geographical region provide appropriate protection for species in a different region. However, this is an important concept for the development of Chinese water quality criteria or standards. Data were assembled on 38 chemicals for which values were available for both native and nonnative species. Sensitivities of these organisms were compared based on the 5% hazardous concentration values and the species sensitivity distribution from a literature review. Results of the present study's analysis showed that there is approximately 74% certainty that use of nonnative species to generate water quality criteria would be sufficiently protective of aquatic ecosystems in China. Without applying any assessment factor to the water quality criteria generated from nonnative species, the uncertainty would be 26% when the native Chinese species might be under protection. Applying an assessment factor of 10 would offer adequate protection to native Chinese species for approximately 90% of tested chemicals and thus reduce the uncertainty from 26% to 10%.
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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 | 0.010 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| 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".