Commercializing Innovation In Residential Energy Retrofits In Toronto: A Case Study Involving Gemeni NTED®
Bibliographic record
Abstract
Retrofitting Canada’s existing housing stock to increase energy efficiency of dwellings is an opportunity to reduce energy consumption and greenhouse gas emissions. Gemini Nested Thermal Envelope Design (NTED®) is an innovative building retrofit that drastically reduces energy consumption. However, this innovation’s potential can only be realized once it has achieved widespread market acceptance. Using Gemini NTED® as a case study, an innovation commercialization model was applied to energy retrofits to aid in establishing an appropriate commercialization strategy for Toronto. Market research conducted within this study identified external factors affecting commercialization, barriers to innovation adoption and competitive forces affecting profitability. Economic valuation evaluated discounted monetary savings from reduced energy consumption. Results show that the retrofit market is moderately attractive and conducive to earning profits. Results related to Gemini NTED® show that Gemini may have commercialization potential for retrofitting older electrically heated homes especially in Canadian provinces with high electricity rates. Results arising from a soon to be completed Gemini NTED® pilot will confirm capital costs and economic benefit.
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 | 0.002 | 0.003 |
| 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 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".