A Decision Making Framework for Sediment Assessment Developed for the Great Lakes
Bibliographic record
Abstract
A rule-based, weight-of-evidence approach for assessing contaminated sediment on a site-by-site basis in the Laurentian Great Lakes is described. Information from four lines of evidence—surficial sediment chemistry, laboratory toxicity, invertebrate community structure and invertebrate tissue biomagnification—is integrated within each line to produce a pass (‘−’) or fail (‘+’) conclusion, then combined across lines resulting in one of 16 outcome scenarios. For each scenario, the current status of the site, interpretation, and management recommendations are given. Management recommendation(s) can range from no action to risk management required (9 of the 16 scenarios). Within each line of evidence, the strength of each response can also be ranked (e.g., score of 1 to 4), providing managers with more information to aid decision options. Other issues that influence scientific management recommendations include site stability, subsurface contamination and spatial extent of effects. The decision framework is intended to be transparent, comprehensive (incorporating exposure, effect, weight-of-evidence, and risk), and minimally uncertain.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.019 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.009 | 0.003 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".