INCORPORATING EFFECTS-BASED APPROACHES INTO ENVIRONMENTAL IMPACT ASSESSMENT TO IMPROVE POST-DEVELOPMENT MONITORING
Bibliographic record
Abstract
Over the last 50 years, improvements in design of industrial facilities have significantly reduced environmental impacts. But impacts still occur and monitoring programs are the main mechanism to inform when modification/implementation of mitigation is needed. Informed decisions require adequate baseline (pre-development) data to predict impacts based on the development’s design and to understand when the post-development environment has changed. An adaptive monitoring plan provides an effective way to evaluate monitoring results and allow for proactive responses to environmental change before impacts become difficult or challenging to reverse. Unfortunately, baseline data gathered during an environmental impact assessment (EIA) is often inadequate to support an effective post-development adaptive monitoring plan. The primary objective of this dissertation was to demonstrate how post-development monitoring can be improved through forethought during the baseline and predictive assessment phases of an EIA.\nInterpreting the key ecosystem attributes through a lens of ecosystem services that fish require defines measurable attributes that should form key components of pre-development and post-development evaluation and can aid in developing consistency across the phases of an EIA. A review of Canadian hydroelectric facility EIAs concluded that adaptive management would be improved if EIAs do more quantitative modeling that links to adaptive monitoring plans based on better pre-development baselines. A case study evaluated how fish surveys could be used to develop thresholds and decision points and concluded that more than four years of data are needed to develop sensitive monitoring and forecast triggers. Consistent times and locations are required for both adult and young-of-the-year sampling to ensure that data can be compared between years.\nPractitioners should consider what is needed for an effective assessment and post-development adaptive monitoring plan early in the EIA process (i.e., before project specific baseline data collection starts) to ensure there is enough data to calculate quantitative models to estimate impacts and to be able to determine when post-development change has occurred. If we don’t have the proper information to evaluate if there is an impact and to define the cause, then monitoring has failed. Improvement of EIA and post-development monitoring will better focus environmental protection and adaptive management.
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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 | 0.014 | 0.025 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.010 | 0.004 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 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".