Building at the Water's Edge: The Role Of Environmental Certification and Monitoring In Waterfront Project Design
Bibliographic record
Abstract
Certification and monitoring programs should become important tools for waterfront designers and planners to ensure projects address the unique challenges of development at the water’s edge. This session will demonstrate the importance of certification programs and monitoring efforts integrating environmental, social and economic issues into pre-design and planning phases of waterfront projects. The use of three different case studies allows us to relate timely and applicable “lessons learned” at multiple scales of planning and design. These examples are: 1) From a site planning perspective: The Port of Bellingham applied LEED ND in their Waterfront District Master Plan and found key differences exist between coastal and terrestrial projects. They found LEED did not adequately address and credit important aspects of the shoreline environment. 2) From a site specific development perspective: The False Creek Development in Vancouver, B.C. applied the Green Shores certification credit rating system after project completion and found that earlier incorporation of this program could have resulted in an improved habitat design. 3) From a site environmental data collection perspective: The use of scientific monitoring at the Olympic Sculpture Park and Seattle Seawall in Seattle revealed the importance for integrating habitat-monitoring data into the planning process to create better shoreline design solutions. The post-construction data from the Olympic Sculpture Park was then used to define aspects of the Seawall project. The case studies and “lessons learned” make two key points: certification and monitoring programs can be used in different ways to incorporate the aquatic environment into the planning process; and second, the design of certification and monitoring programs and must be closely linked with project design to facilitate positive outcomes for our aquatic environment.
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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 | 0.019 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.011 | 0.013 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".