Anatomy of a decision III: Evaluation of national disposal at sea program action level efficacy considering 2 chemical action levels
Bibliographic record
Abstract
Abstract The potential performance (i.e., ability to separate nontoxic from toxic sediments) of a range of international Disposal at Sea (DaS) chemical Action Levels (ALs) was compared using a sediment chemical and toxicological database. The use of chemistry alone (without the use of further lines of evidence) did not perform well at reducing costs and protecting the environment. Although some approaches for interpreting AL1 results are very effective at filtering out the majority of acutely toxic sediments, without subsequent toxicological assessment, a large proportion of nontoxic sediments would be unnecessarily subjected to treatment and containment, and a number of sublethally toxic sediments would be missed. Even the best tiered systems that collect and evaluate information sequentially resulted in the failure to catch at least some sublethally or acutely toxic sediments. None of the AL2s examined were particularly effective in distinguishing between non-, sublethally, or acutely toxic sediments. Thus, this review did not support the use of chemical AL2s to predict the degree to which sediments will be toxic. Integr Environ Assess Manag 2017;13:1086–1099.© 2017 The Authors. Integrated Environmental Assessment and Management Published by Wiley Periodicals, Inc. on behalf of Society of Environmental Toxicology & Chemistry (SETAC). Key Points Chemistry alone performs poorly at reducing costs and protecting the environment. Some chemical Action Level (AL) 1 strategies filter out the majority of toxic sediments, but with high false positives. Many tiered systems were able to discriminate most toxic and nontoxic sediments. None of the AL2s examined were effective in distinguishing between nontoxic and toxic sediments.
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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) | 0.000 | 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".