Designing Green Architecture Building that Blend with the Nature
Bibliographic record
Abstract
The urbanized built environment is expanding rapidly in developing nations, and there is an urgent need to implement green building principles to make design and construction methods there more sustainable. Cities have seen an increase in built-up areas, and new construction projects that use the green building idea can undoubtedly lessen the environmental impact of buildings. However, the region surrounding urban settlements was likely occupied prior to the second millennium BC, and there is proof that people have lived there continuously from at least the sixth century B.C. Therefore, in addition to new construction, there is a significant chance that existing structures could have a negative environmental impact if their maintenance and operation practices are not examined. Government regulations pertaining to energy and water use as well as CO2 emissions will need to include mandatory limitations. The performance of existing buildings can be enhanced by a number of important sustainability improvements, in addition to energy and water efficiency, such as structural evaluation, resource use, disaster resilience, waste reduction through recycling programs, sustainable procurement and purchasing practices, and continuing operations and maintenance practices. Employee comfort and indoor environmental quality are two "intangible" benefits of green buildings that are difficult to measure but just as crucial to consider as the tangible ones. "Indoor Environmental Quality (IEQ)" includes things like views, air quality, natural lighting, thermal and physical comfort, and the ability to manage one's surroundings, all of which have beneficial psychological and physical benefits and help make residents happier and healthier.
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.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".