Development and Analysis of Simplified Control-oriented Models for a Group of Institutional Offices
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Most zone or room level control-oriented models are developed with opaque and varying workflows. The workflows vary depending on whether the models are white or black box. In this paper the focus is applied to inverse modelling. The contribution of this paper is the application of a simple prescribed workflow for generating inverse models of a group of offices that can be applied by those less experienced with inverse modelling. The goal of the models is to predict indoor air temperature at the next time step for each room with reasonable accuracy. Measurements were taken in 27 offices for a period of one month. These data were used to fit and validate office level control-oriented inverse models using the MATLAB System Identification Toolbox. The maximum mean absolute error (MAE) for any office over the two-week validation period was 0.31°C and the average MAE for all 27 offices was 0.15°C.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 it