Bibliographic record
Abstract
In Iran, the buildings sector is responsible for large consumption of energy and corresponding GHG (Greenhouse Gases) emissions.The insulation of buildings is a relevant technology to reduce such energy consumption and GHG emissions.According to Paris agreement 2016 all countries in the world are obliged to reduce their GHG emission and Iran as a developing country should contribute to international agreement.This paper seeks Iran's building sector role in mitigating national GHG emission.The main objective of this study is to minimize the building environmental impacts by proper insulation.For this purpose, five details for applying thermal insulation thickness, recommended by Iran national building code were selected.The most conventional thermal insulation materials produced in Iran are selected as thermal insulation material.First, thermal insulation thickness was optimized for all surfaces comprising a single cubic thermal zone in climate of Tehran.The results are evaluated in reducing, net environmental saving and energy demand for a typical residential building located in Tehran.The results of this study represents optimized insulation thickness for all building envelope surfaces, in accordance to mandatory thermal performance, in order to optimize energy consumption of the building and minimizing its environmental footprint.The results shows that by accommodating proper insulation total environmental impact of building can reduce to more than 70% percent.
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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.931 | 0.926 |
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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
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".