REVITALIZING THE ROCHESTER GORGE Strategic Urban Site Planning to Enhance Water Quality in the Lower Genesee River
Bibliographic record
Abstract
The project began with investigating a regional asset of interest: the Genesee River. Initial research into this area revealed that the river suffers from poor water quality. The causes and effects of poor water quality were explored, and it was determined that excess nutrients – namely phosphorus – play a critical role in the water quality of the Genesee River and present downstream risk at the river’s point of outflow in Lake Ontario. An examination of peer-reviewed literature, contemporary examples in the built environment where similar water quality issues were addressed, and existing watershed design frameworks were conducted to understand water quality issues and potential solutions. The methodology involved the analysis of examined literature and precedents, which led to the formulation of an inventory of potential problem-solving strategies. This was followed by a sequence of steps to synthesize project research into a decision-support tool for selecting interventions that address water quality issues. A preliminary design exercise on a site within the City of Rochester adjacent to the Genesee River demonstrated the decision-support tool’s function to solve the identified problem and reveal strengths and weaknesses when spatially applied. While the design showed potential in reducing the Genesee River’s excess nutrient volume, its effectiveness was limited. Due to its comprehensive approach, it was concluded that the decision-support tool may be a beneficial addition to the early stages of urban site planning with a focus on water quality management or existing watershed plan development. The expanded inclusion of considerations absent from this project could lead to more robust project outcomes, but additional research is required.
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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 | 0.007 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| 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".