Anatomy of a decision II: Potential effects of changes to Tier I chemical approaches in Canadian Disposal at Sea program sediment assessment protocols
Bibliographic record
Abstract
Abstract The effects of possible changes to the Canadian 2-tiered assessment framework for dredged material based on outcomes of the 2006 Contaminated Dredged Material Management Decisions Workshop (CDMMD) are evaluated. Expanding on the “data mining” approach described in a previous paper, which focused solely on chemical lines of evidence, the efficacy of Tier 1 approaches (increases to the number of chemical analytes, use of mean hazard quotients, and the use of a screening bioassay) in predicting toxicity are evaluated. Results suggest value in additional work to evaluate the following areas: 1) further expanding minimum chemical requirements, 2) using more advanced approaches for chemical interpretation, and 3) using a screening-level bioassay (e.g., Canadian solid-phase photoluminescent bacteria test) to determine whether it would complement Tier 1 chemistry as well as or better than the solvent-based Microtox™ test method evaluated in the present study. Integr Environ Assess Manag 2017;13:1072–1085. © 2017 The Authors. Integrated Environmental Assessment and Management published by Wiley Periodicals, Inc. on behalf of Society of Environmental Toxicology & Chemistry (SETAC) Key Points We developed a sediment database to examine international dredged material assessment approaches. A longer chemical action list is more effective at predicting toxicity. Well-designed mean hazard quotient approaches outperform “one out/all out” rules. A conservative Tier 1 screening bioassessment improved toxic detection.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 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 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".