Bibliographic record
Abstract
This research investigates the application of inverse green roof models to both characterize green roof thermophysical properties and simulate the heat flux through the roof assembly.The first chapter of this thesis presents a preliminary analysis of green roof thermal performance in Ottawa, Canada, using two years of measured data, wherein thermal performance is defined as the percent reduction in total monthly heat exchange promoted by the green roof, relative to that through an adjacent conventional roof.Climatic factors influencing thermal performance are discussed on a seasonal basis, concluding that the green roof functioned best during warmer months when evapotranspiration was likely to be greatest.Thermal performance ranged between 31 -63% from May through September, with reduced performance during the colder months.In chapter 2, two inverse models of varying spatial discretization are developed for the same green roof: (1) a resistor-capacitor (RC) thermal network model, and (2) an implicit finite difference (FD) model.Each model was calibrated using monthly data from May to September in 2016 by employing a genetic algorithm to extract the thermophysical properties of the green roof soil and canopy layers via multi-linear regression.The difference in spatial resolution between each model was identified as an influential factor to thermophysical property estimation during calibration.The calibrated models were used to predict hourly rates of heat flux through the structural component of the green roof over each month of the study period, resulting in a root-mean-squared-error between 0.51 -1.0 W/m 2 and 0.41 -0.81 W/m 2 for the RC and FD models, respectively.Both models were validated against 5 continuous months of data from 2017, demonstrating that inverse modelling can successfully generate realistic thermophysical green roof properties.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".