The Principles and Methods of Deriving and Determining Remediation Criteria for Contaminated Soils
Bibliographic record
Abstract
The remediation standard for contaminated soils is an indispensible part of the soil-environmental standard system. In response to the requirement of assessing the remediation efficiency of contaminated soils and handling emergency pollution incidents, the enactment of the remediation standards for contaminated soils based on the actual situation in China is urgently needed. Moreover, research on the remediation criteria for contaminated soils can provide with data support and scientific grounds for the enactment of remediation standards for contaminated soils. Thus, the development of the remediation criteria for contaminated soils is of great significance in practice. The connotation of the remediation criteria of contaminated soils was firstly explained in brief. Then, USA, Canada, the Netherland and Denmark were chosen as the typical examples, in particular, the principles of deriving the remediation criteria for contaminated soils were elaborated, and the method system of deriving and determining the remediation criteria for contaminated soils was summarized. In the end, future research on contaminated soil remediation criteria was 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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.008 | 0.004 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".