Unifying Visualization of Hydrologic, Thermal and Plant Growth Performance in Green Roofs
Bibliographic record
Abstract
Vegetated roofs have become an important component of sustainable building design in many cities across the world due to the range of environmental benefits they provide, including water retention, evaporative cooling, and biodiverse habitat. Not all green roofs are made equal, and the performance metrics of green roofs are influenced by the choice of growing media, planting, and the use of supplemental irrigation, among other factors. There is a need for additional studies on the influence of multiple design variables on multiple performances in green roofs, as well as for visualization and design tools that represent such complex relationships. This paper describes the data acquisition system of a replicated green roof modular array to derive hydrologic, thermal, and plant growth data over a three-year period. Using Rhinoceros™ with Grasshopper® and LunchBox™ plug-in components, as well as a web-based platform, an interactive tool was developed to unify visualization of diverse forms of data. We discuss the tool's merits over current visualization practices and the potential use in green roof design simulation by researchers and design professionals.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".