Performance Assessment of a CO <sub>2</sub> -Based Demand-Controlled Frost Resilient Dual-Core Energy Recovery Ventilation System for Northern Housing
Bibliographic record
Abstract
To better address indoor air quality (IAQ) and mold issues in northern housing experiencing varying occupancies and indoor conditions, ventilation needs to become demand-controlled. Currently, heat/energy recovery ventilators (HRVs/ERVs) are commonly installed in northern communities and they offer constant or globally controlled airflows. Overcrowded homes are then under-ventilated, leading to higher indoor pollutants and moisture that need to be controlled. This study examines a method for providing adequate ventilation through control of ventilation based on occupancy and modulation of ventilation fans. This paper presents results from a side-by-side testing of a CO2-based demand-controlled dual-core ERV versus conventional single-core ERV with constant flow using twin houses with simulated occupancies. The implemented strategy based on a CO2 sensor network connected with a dual-core ERV continuously exhausting stale air from the kitchen and bathrooms was simple and efficient in adjusting ventilation rate based on occupancy rate. The potential of the CO2-based demand-controlled dual-core ERV system was evaluated based on its capability to control indoor CO2 levels, percentage of time kept below 1,000 ppm, and power consumption.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".