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 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 | 0.052 | 0.063 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".