Investigation of LSF Structure Effect on Economy and Sustainable Housing in Iran. Case Study: 50m2 Units
Bibliographic record
Abstract
Housing is an important element in government’s broad social agenda. This element overlaps employment, health, education, crime and many other aspects of life. We can say housing is stable, when everyone has access to a minimum house, social cohesion promotes and people’s lives improve toward self-reliance. On the other hand, designing stable buildings is aimed to reduce its damage on environment, energy resources and nature that requires spending to build housing, however, due to high cost of housing, poor sectors of society cannot buy house, while government has to provide house for all sectors in society. Assuming that LSF structures promote stability, this study is aimed to structural search to build urban buildings, which is responsive to sustainable architecture and can be justified economically. This study is analytical and based on library studies and comparative analogy tries to answer questions such as: how to use LSF structure in designing buildings in order to reduce their cost, and whether these structures is affordable across the country based on variety of building regulations in cities. In this regard, in order to examine LSF technology in construction and comparing it with conventional construction methods, we investigate and calculate 50 square meters one-bedroom apartments. Our conclusion indicates that using this technology in addition to positive response to sustainable architecture, increases the ability to buy a home, as well as strength and durability of these structures compared to conventional constructions in different environmental conditions and earthquake.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".