Bibliographic record
Abstract
Alternative sources of energy are being sought after in the world today, as the availability of fossil fuels and other non-renewable resources are declining. Solar energy offers a promising solution to this search as it is a less polluting renewable energy resource and can be easily converted into electricity through the usage of photovoltaic systems. This thesis focuses on the modeling of urban solar energy with high spatiotemporal resolution. A methodology was developed to estimate hourly solar PV electricity generation potential on rooftops in an urban environment using a 3-D model. A case study area of Ryerson University, Toronto was chosen and the incident solar radiation upon each building rooftop was calculated using a software tool called Ecotect Analysis 2011. Secondly, orthophotos of the case study area were digitized using Geographic Information Systems in order to eliminate undesirable rooftop objects within the model. Lastly, a software tool called HOMER was used to generate hourly solar PV electricity estimates using the values generated by the other two software tools as input parameters. It was found that hourly solar PV output followed the pattern of a binomial curve and that peak solar generation times coincided with summer peak electricity consumption hours in Ontario.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".