Nature-Based Solutions for Arctic Housing through Intermediate Spaces
Bibliographic record
Abstract
This research explores the potential of intermediate spaces as architectural solutions for incorporating nature-based approaches into public housing models designed for extreme cold climates. Intermediate spaces, situated between indoor and outdoor environments, foster positive connections to nature. Prior studies emphasize their potential to enhance occupant well-being through increased outdoor connectivity, while also serving as productive and affordable spaces. These spaces can feature transparent surfaces to maximize natural light, making them suitable for plant cultivation and greenery integration. The objective of this study is to optimize architectural parameters for intermediate spaces to support greenery production effectively. Specifically, the research aims to maintain indoor temperatures within an optimal range of 13–27°C – optimum temperature for plant growth - and maximize solar gain for plant growth. A numerical simulation model was developed to evaluate the performance of intermediate spaces by varying architectural parameters, including (1) transparency ratio and (2) space depth. Findings reveal that intermediate spaces with a transparency ratio of 40–60% and a depth of 5-7 meters achieve the highest duration of optimal temperature conditions and maximum solar gain, supporting plant growth and enhanced daylight exposure. These results demonstrate that integrating intermediate spaces into public housing models in extreme cold climates can contribute to Canada’s food security initiatives, particularly in Northern regions, by promoting sustainable indoor plant cultivation. This research underscores the value of nature-based solutions in addressing food security and enhancing the livability of public housing in harsh environments.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".