Important considerations in the derivation of background at sediment sites
Bibliographic record
Abstract
In the United States, there is an absence of federal guidance related to deriving and applying background concentrations at contaminated sediment sites. This absence has resulted in significant variability, uncertainty, and disagreement regarding how representative background concentrations of chemicals of concern should be derived for these sites. The present article discusses important considerations in the derivation of representative background concentrations to be used in the evaluation of contaminated sediment sites. Specifically, a thorough understanding of a site is critical to selecting the background reference areas from which representative background concentrations can be derived, representative background concentrations should account for contributions from those background chemical inputs (natural and anthropogenic sources) that will continue affecting the site even after remediation, perceived outliers should not be eliminated from the background data set just because they are the highest or lowest values, and geochemical evaluation of trace metals is a useful tool for deriving representative background concentrations. On a site-specific level, representative background concentrations are critical for putting site-related risk into context, developing a cost-effective and technically feasible remedial approach, understanding the potential for recontamination, and ensuring long-term remedy success. In a broader context, clear guidance from the United State Environmental Protection Agency (USEPA) for deriving and applying background concentrations for contaminated sediment sites would help promote national consistency in site assessment and remedy decision making. Integr Environ Assess Manag 2019;00:000-000. © 2019 The Authors. Integrated Environmental Assessment and Management Published by Wiley Periodicals, Inc. on behalf of Society of Environmental Toxicology & Chemistry (SETAC).
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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 teacher head, 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".