Integrating environmental monitoring with cumulative effects management and decision making
Bibliographic record
Abstract
Abstract Cumulative effects (CE) monitoring is foundational to emerging regional and watershed CE management frameworks, yet monitoring is often poorly integrated with CE management and decision-making processes. The challenges are largely institutional and organizational, more so than scientific or technical. Calls for improved integration of monitoring with CE management and decision making are not new, but there has been limited research on how best to integrate environmental monitoring programs to ensure credible CE science and to deliver results that respond to the more immediate questions and needs of regulatory decision makers. This paper examines options for the integration of environmental monitoring with CE frameworks. Based on semistructured interviews with practitioners, regulators, and other experts in the Lower Athabasca, Alberta, Canada, 3 approaches to monitoring system design are presented. First, a distributed monitoring system, reflecting the current approach in the Lower Athabasca, where monitoring is delegated to different external programs and organizations; second, a 1-window system in which monitoring is undertaken by a single, in-house agency for the purpose of informing management and regulatory decision making; third, an independent system driven primarily by CE science and understanding causal relationships, with knowledge adopted for decision support where relevant to specific management questions. The strengths and limitations of each approach are presented. A hybrid approach may be optimal—an independent, nongovernment, 1-window model for CE science, monitoring, and information delivery—capitalizing on the strengths of distributed, 1-window, and independent monitoring systems while mitigating their weaknesses. If governments are committed to solving CE problems, they must invest in the long-term science needed to do so; at the same time, if science-based monitoring programs are to be sustainable over the long term, they must be responsive to the more immediate, often shorter term needs and CE information requirements of decision makers. Integr Environ Assess Manag 2018;14:407–417. © 2018 SETAC Key Points The integration of environmental monitoring with emerging cumulative effects management and decision-making frameworks faces organizational and institutional challenges more so than scientific or technical ones. Three basic options or approaches to integration exist—distributed monitoring, 1-window approach, and independent cause–effect science—each with its own strengths and limitations regarding advancing cumulative effects science versus supporting regulatory decision-making needs. The most appropriate design for monitoring integration requires agreement on the types of questions and confidence for determining causal relationships in cumulative effects science and understanding what is required to support regulatory decision making. Successful cumulative effects management frameworks require both a long-term commitment to monitoring to understand and advance cumulative effects science and monitoring programs that can respond to emergent problems and the day-to-day needs of regulatory decision makers.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.003 | 0.008 |
| Scholarly communication | 0.011 | 0.005 |
| Open science | 0.004 | 0.008 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".