LEED Canada energy performance modelling of a medical office building using Carrier HAP and NRCan EE4
Bibliographic record
Abstract
The specific goal of this project is to model the energy performance of a medical office building in Carrier HAP and NRCan EE4 simulation software in order to qualify for LEED Canada energy and atmosphere perquisite 2 and credit 1. LEED Canada requires that to be eligible for EAp2 and EAc1, the proposed building must be 25% more energy efficient than a reference building which is designed according to Model National Energy Code for Buildings (MNECB). The demise of EE4 has created a demanding need to look for substitute software. One of the tasks of the project is to analyze HAP for EAp2 and EAc1 compliance process. EE4 generates the MNECB reference building itself but in HAP the reference building has to be modelled manually. The results from HAP and EE4 show that energy savings are 39.10% and 38.31% respectively with respect to MNECB reference building.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".