Proposed Approach of Korean Ecological Risk Assessment for the Derivation of Soil Quality Criteria
Bibliographic record
Abstract
Ecological Risk Assessment (ERA) supports a decision-making process such as establishment of environmental quality criteria. Soil quality criteria (SQC) are essential to protect soil organisms from the exposure to various soil contaminants. In this study, ERA methodologies of advanced countries for soil pollution were extensively compared to propose the ERA approach suitable for soil ecosystem in Korea. The soil ERAs in European Chemical Bureau(ECB), The Netherlands, and Canada can be classified as deterministic ecological risk assessment (DERA), and probabilistic ecological risk assessment (PERA) based on species sensitivity distribution (SSD). We propose three ERA methods according to abundance and reliability of soil ecotoxicity data. The method considered land use such as residential/agricultural, and industrial/commercial uses. The taxonomic groups of soil organism were classified as 'Class' level including different trophic levels (Magnoliopsida or Liliopsida, Clitellata, and Insecta or Secernentea). This study can be used to estimate the soil quality criteria to protect soil biota.
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".