An Adaptive Environmental Effects Monitoring Framework for Assessing the Influences of Liquid Effluents on Benthos, Water, and Sediments in Aquatic Receiving Environments
Bibliographic record
Abstract
Environmental effects monitoring (EEM) has been traditionally used to evaluate the effects of existing facilities discharging liquid effluents into natural receiving waters in Canada. EEM also has the potential to provide feedback to an ongoing project in an adaptive management context and can inform the design of future projects. EEM, consequently, can and should also be used to test the predictions of effects related to new projects. Despite EEM's potential for widespread applicability, challenges related to the effective implementation of EEM include the use of appropriate study designs and the adoption of tiers for increasing or decreasing monitoring intensity. Herein we describe a template for designing and implementing a 6-tiered EEM program that utilizes information from the project-planning and predevelopment baseline data collection stages to build on forecasts from the initial environmental impact assessment project-design stage and that feeds into an adaptive management process. Movement between the 6 EEM tiers is based on the exceedance of baseline monitoring triggers, forecast triggers, and management triggers at various stages in the EEM process. To distinguish these types of triggers, we review the historical development of numeric and narrative triggers as applied to chemical (water and sediment) and biological (plankton, benthos, fish) endpoints. We also provide an overview of historical study design issues and discuss how the 6 EEM tiers and associated triggers influence the temporal-spatial experimental design options and how the information gained through EEM could be used in an adaptive management context. Integr Environ Assess Manag 2018;14:552-566. © 2018 SETAC.
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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 | 0.027 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.005 | 0.005 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".