Integrating Multiple Toxicological Endpoints in a Decision-Making Framework for Contaminated Sediments
Bibliographic record
Abstract
Contaminated sediment has been identified as one of the major impediments to ecosystem restoration, but there has been little progress made in the management of sediment contaminants. Four primary lines of evidence are generally required for informed assessments yet the integration of these various lines of evidence is problematic. Using data from 220 reference sites located in the nearshore zone of the Laurentian Great Lakes the normal response of four species of laboratory organisms to sediments representing a wide range of sediment characteristics was examined. The toxicity data from the reference sites were used to establish categories of responses to test sediments. The delineations for the three categories were developed from the standard statistical parameters of population mean and standard deviation (mean ± SD) of an endpoint measured in all reference sediments. Three approaches for integrating information were examined; the first two are score based, the third approach uses a multivariate statistical method to integrate the responses. The methods were examined using both artificial and real test site data and from this it was concluded that ordination is the superior of the three. It is the least subjective within the context of the integration of the endpoints, is quantitative, and also provides appropriate weighting based on the variation observed within reference sites.
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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 | 0.029 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| 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".