SEDS: setting environmental decisions for sediment, a decision making tool for sediment management
Bibliographic record
Abstract
Abstract The need for guidance on the management of contaminated sediment has been articulated by the International Joint Commission, scientists and resource managers in many jurisdictions. There is a growing convergence on what constitutes a valid comprehensive sediment assessment but active debate on how to synthesize multiple pieces of information on sediment chemistry, biological information from field monitoring and laboratory sediment bioassessment. Recognizing the current state of knowledge, we provide a recommended approach to bioassessment sediment management strategies. The intent is to facilitate the formulation of data interpretation tools needed for a decision making process that is flexible to enable site-specific determination regarding the need to take action beyond the control of sources of contamination. While the concepts contained herein have been employed implicitly in Canada and Ontario, the details on data collection, evaluation, and reaching a management decision are explicitly laid out in this paper. It is expected that field application of this approach could lead to modifications of this framework.
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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 | 0.002 | 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 | 0.001 | 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) | 0.001 | 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".