Principles and Challenges for Multi-Stakeholder Development of Focused, Tiered, and Triggered, Adaptive Monitoring Programs for Aquatic Environments
Bibliographic record
Abstract
In Canada, there is almost 30 years of experience in developing tiered and triggered adaptive monitoring programs focused on looking at whether environmental concerns remain when pulp and paper mills, or metal mines, are in compliance with their discharge limits. These environmental effects monitoring programs were based on nationally standardized designs. Many of the programs have been developed through multi-stakeholder working groups, and the evolution of the program faced repeated frictions and differing opinions on how to design environmental monitoring programs. This paper describes key guidance to work through the initial steps in program design, and includes scientific advice based on lessons learned from the development of the Canadian aquatic environmental effects monitoring program.
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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 | 0.232 | 0.080 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.023 | 0.036 |
| Scholarly communication | 0.019 | 0.014 |
| Open science | 0.010 | 0.021 |
| Research integrity | 0.010 | 0.017 |
| Insufficient payload (model declined to judge) | 0.004 | 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".