Architecture as a “nature recorder”: Demonstrates sustainability of buildings in response to environmental degradation
Bibliographic record
Abstract
Architectural sustainability is an important part of architectural design. In addition to materials, economy, and environmental sustainability, the current increasing frequency of natural disasters and environmental degradation also reminds that the sustainability of buildings in response to landscape and environmental degradation has become a new aspect. Landscape degradation and environmental degradation are causing varying degrees of negative impacts on many buildings. Unfortunately, most current architectural designs feel powerless in dealing with the increasingly intensified environmental crisis that cannot be ignored. The sustainability of buildings in response to environmental and landscape changes is rarely discussed. This work explores the concept of landscape sustainability in response to environmental degradation. Through imitating and replicating natural landscapes, it achieves the ability of human intervention that belongs to the environment and residents. The research started by analyzing some trends in the problems caused by landscape deficits in recent decades and identified the Capilano lake in North Vancouver as a specific research site, paying particular attention to its natural characteristics and dynamic interaction with man-made landscapes. After choosing the North Vancouver area as a case study, the focus of the study is how to adapt the design concept of “nature recorder”. Regard new buildings as viable systems that can adapt to and respond to climate and environmental changes. Through the concept of intersection, architecture creates communication and balance between nature and man-made, light, and shadow.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".