Bibliographic record
Abstract
Synopsis This paper presents a case for application of automated monitoring, analysis and diagnostic tools for monitoring-based commissioning. Selected examples are presented in which such tools have been used successfully to support commissioning activities in southwestern Canada and the U.S. Pacific Northwest. The first example involves use of spreadsheet-based tools to automatically generate diagnostic plots that are visually examined for specific features that reveal operational problems in space conditioning systems of large commercial buildings. The findings then guide re-tuning actions to increase building energy efficiency. This is followed by application of a tool for continuous monitoring of whole-building energy use to automatically track energy savings resulting from a utility commissioning program. This tool also provides a means by which to detect degradation of savings and performance to guide monitoring-based commissioning actions. The potential use of automated diagnostic tools for chillers and packaged air conditioners is then described for continually commissioning these units. The paper concludes with a discussion of the impacts of this approach on commissioning, including potential time savings, associated cost savings, and improvements in the quality of commissioning.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.011 | 0.003 |
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".