Toward a Parametric Model for Major Household Systems Performance
Bibliographic record
Abstract
With the residential sector representing approximately 16.6% of total energy consumption in Canada (OEE, 2006) and 21% in the United States(DOE, 2008), decisions that homeowners make on upgrades in their homes can have a large impact on national energy usage and greenhouse gas emissions. Since there remains a large amount of aging housing stock in Canada and worldwide, it is important to focus on educating the homeowners themselves in order to have a positive effect on home renovation projects. In fact, many studies have been conducted to survey and estimate the current state of national building stock in: Canada (Parekh, 2005), U.S.A. (Persily et al, 2006), Europe (Petersdorf et al, 2006), and Japan (Shimoda et al, 2003).These databases of building stock can help to identify major areas needing renovations in order to decrease residential total energy usage. Also, inclusion of these databases into the model would help to identify the best choice of defaults for a given user, based on information from the databases.
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".