Deriving site-specific clean-up criteria to protect ecological receptors (plants and soil invertebrates) exposed to metal or metalloid soil contaminants via the direct contact exposure pathway
Bibliographic record
Abstract
Abstract Soil contaminant concentration limits for the protection of terrestrial plants and soil invertebrates are commonly based on thresholds derived using data from laboratory ecotoxicity tests. A comprehensive assessment has been made for the derivation of ecological soil screening levels (Eco-SSL) in the United States; however, these limits are conservative because of their focus on high bioavailability scenarios. Here, we explain and evaluate approaches to soil limit derivation taken by 4 jurisdictions, 2 of which allow for correction of data for factors affecting bioavailability among soils, and between spiked and field-contaminated soils (Registration Evaluation Authorisation and Restriction of Chemicals [REACH] Regulation, European Union [EU], and the National Environment Protection Council [NEPC], Australia). Scientifically advanced features from these methods have been integrated into a newly developed method for deriving soil clean-up values (SCVs) within the context of site-specific baseline ecological risk assessment. Resulting site-specific SCVs that account for bioavailability may permit a greater residual concentration in soil when compared to generic screening limit concentrations (e.g., Eco-SSL), while still affording acceptable protection. Two choices for selecting the level of protection are compared (i.e., allowing higher effect levels per species, or allowing a higher percentile of species that are potentially unprotected). Implementation of this new method is presented for the jurisdiction of the United States, with a focus on metal and metalloid contaminants; however, the new method can be used in any jurisdiction. A case study for molybdate shows the large effect of bioavailability corrections and smaller effects of protection level choices when deriving SCVs. Integr Environ Assess Manag 2014;10:346–357. © 2014 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals, Inc. on behalf of SETAC. Key Points Scientifically advanced features have been integrated into a newly developed method for deriving soil clean-up values (SCVs) for metals and metalloids within the context of site-specific baseline ecological risk assessment. This new SCV method was developed specifically for protection of terrestrial plants and soil invertebrates, however it can include protection of soil microbial processes when appropriate microbial data are available for inclusion along with plant and invertebrate data.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".