Analyzing the Impact of the Phius HRV/ERV protocol on North American Passive House Certification
Bibliographic record
Abstract
The stated heat recovery efficiency of HRV and ERV units in North American passive houses is dependent on the testing procedures and calculation methods established by several pertinent performance testing standards. This project highlights major differences between the applicable HRV/ERV standards for North American passive houses: the European Passive House Institute standard, the Canadian CSA-439-09 standard, and the American HVI-920 standard. It further examines the proposed PHIUS protocol which established ɳPHUIS, a modified HRV/ERV heat recovery efficiency rating to more accurately reflect the North American climate. Simulations were performed to quantify its effect on the modelled annual heat demand for 31 certified passive houses. The results yielded two key findings. First, the margin of error for the new rating, ɳPHUIS, relative to the existing rating, Ɛ, is a function of the regional climate given by the equation: y = 0.00001x + 0.0012. Locations with a colder climate have longer winters, thereby increasing the heating demand and intensifying the margin of error. Second, small to medium sized houses with floor areas (<250m2), which formed 90% of the sample study, have the largest impact on the margin of error up from 3.8% to 12% compared to large homes (>250 m2) from 2.8% to 4.2%. The results validate the necessity for PHIUS’ proposed ɳPHUIS for North American HRV/ERVs.
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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 | 0.011 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".