Nature-Based Solutions for Arctic Housing through Intermediate Spaces
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 it