Limits of a single surrogate model development methodology to represent housing stocks
Bibliographic record
Abstract
Surrogate models for predicting housing stock thermal comfort, and operational cost, energy or emissions have increasingly gained popularity due to the potential to identify solutions to accelerate and optimise construction. In addition to the potential opportunity to progress towards climate goals and increase resource efficiencies by identifying areas of interest, they are much less time consuming than brute-force simulations of millions of scenarios. Although they are popular due to their promise, analysis of scalability limits for surrogate model development methodologies have been left out. Without this analysis, assurance that these may be used over differing housing stock scales cannot be provided. This work uses black-box model interpretation as well as common predictive performance metrics to assess the scalability of the development methodology presented. The novelty of this work stems from its comparison of the performance of a development methodology through training a learning algorithm with a progressively varied dataset to illustrate its scalability. This validates the use of a single surrogate model to represent numerous bottom-up archetypes used to represent a housing stock.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".