An Investigation into Sustainable Building Evaluation Strategies for use within the Canadian Forces and the Department of National Defence
Bibliographic record
Abstract
Environmental sustainability in regards to infrastructure and the built environment is a significant consideration for the Department of National Defence (DND).As the 2 nd largest landowner in Canada, the DND is making great efforts to operate in a sustainable manner through the creation and implementation of sustainable building policies.Meeting the green/sustainable buildings requirements of the recent Federal Sustainable Development Strategy, and the 2006 DND Sustainable Development Strategy to achieve accreditation within generic green building systems, has proven to be extremely challenging given the mandate and the nature of Canadian Forces (CF) operations, both domestically and overseas.This line of research was conducted in order to identify the unique requirements and the sustainability factors/criteria that are applicable to the CF / DND and to investigate how to best align these requirements within the context of sustainable development strategies.Input data was obtained through an analysis of literature and relevant documents, as well as a series of interviews with individuals at all levels within the Federal Government.The result of this research undertaking was, in part, the creation of a Sustainable Development for DND based on a thorough, comprehensive and internationally-investigated factor selection process.These results illustrate the importance for the DND to consider implementing a department-specific sustainable building strategy; one that is more representative of the unique type of infrastructure DND owns and operates.The results of such an investigation can surely be used to assist with best practices and strategic policies within DND and to potentially influence policies at the Federal level of governance in this regard.The ultimate goal of this thesis was to establish the policy framework with a view for DND to develop and implement its own unique Sustainability Standard.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".