Review on the Aquatic Organisms Toxicity Test in the Whole Effluent Assessment
Bibliographic record
Abstract
Wing the industrialization and urbanization,the industrial and municipal waste water becomes the main pollution source to ambient water bodies. Developed countries have established the whole effluent toxicity assessment technology system for the the complex waste water with the implementation of the best applicable technology(BAT) policy. They applied ecological tests and evaluation indices in the waste water management. Test methods of effluent biological toxicity obviously vary among the countries and regional organizations. The USA,Canada and Australia developed a number of methods for testing native species consicering their vast territories,diverse climate types and biological diversity. The European regional organizations and the Germany government paid much attention to development their standard methods for the model species,as well as genotoxicity and endocrine disruption. It makes the testing results from different countries comparable within the same frame,and helps to uniform the discharge limits. New Zealand and the UK are island countries with short rivers. They paid much attention to the coastal ecosystem,so that more toxicity tests were developed for marine organisms. Since the research on the effluent toxicity in China was lately started from the 1980s,it is far away from the requirement of management application. The establishment of the effluent toxicity testing technology system in China should be accomplished step by step by learning the experience from developed countries.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".