THE DEVELOPMENT OF A TEST PROTOCOL FOR AN ON-LINE WHOLE-BUILDING ENERGY ANALYSIS TOOL FOR HOMEOWNERS
Bibliographic record
Abstract
This paper presents the testing of a dynamic on-line whole-building energy calculator. The Home Energy Analyser is intended for use by the Canadian public as a first step in assessing the energy efficiency of their houses. The tool consists of a web-based interface for user inputs; an application to create the simulation input files from these user inputs; a simulation engine; and an application for passing the simulation engine output back to the interface and user. A detailed test protocol, composed of three parts, was developed as part of the software development process for quality assurance purposes. These three parts include: • testing the functionality of the user interface in passing the user inputs to the application that generates the input files for the simulation engine; • testing the functionality of the mapping between the user interface output and the simulation engine input files; and • testing the mapping between the simulation engine output and the web interface; This paper will investigate the three parts of the test plan as well as the task automation tools developed to facilitate the testing. The use of these tools proved very useful given the large number of combinations of user inputs at the web interface and input files to the simulation engine. The findings show the importance of having a detailed and comprehensive test protocol during the software development phase.
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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 | 0.023 | 0.078 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".