Advances and Prospect of Research on Environmental Criteria/Benchmarks and Enactment of Environmental Standards
Bibliographic record
Abstract
To derive and develop environmental criteria/benchmarks is a challenging and intractable scientific conundrum.The enactment and revision of environmental standards is a long-term and arduous task.In this review,the conception and connotation of environmental criteria/benchmarks and standards were firstly expounded,and the old cognition in the past was rectified according to their recent international developments in this direction.It emphasizes that the environmental criterion/benchmark is not equal to the environmental quality criterion/benchmark,and environmental criteria/benchmarks should be comprised of environmental quality criteria/benchmarks and remediation/cleanup criteria/benchmarks for contaminated environment;correspondingly,environmental standards should include both environmental quality standards and remediation/cleanup standards for contaminated environment.On the basis of the new cognition,prominent accomplishments acquired in the field of environmental quality criteria/benchmarks at home and abroad have been summarized,some advanced modus operandies for environmental quality standards in France(as a representative of west European countries),Canada(as a representative of American countries) and Italy(as a representative of midland European countries) were enumerated,and relevant progresses in research on remediation/cleanup criteria/benchmarks for contaminated environment and enactment of remediation/cleanup standards for contaminated environment are introduced.Future research on environmental criteria/benchmarks has been prospected.
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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 | 0.036 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.002 | 0.014 |
| Scholarly communication | 0.011 | 0.020 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.006 |
| 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".