Architectural Green Spaces Design of Medical Centers with Passive Defense Approach
Bibliographic record
Abstract
Since medical centers, from the perspective of civil defense, are considered as centers of critical urban land, therefore, it is essential the criteria and defensive mechanisms to comply in design. One of these strategies would be taking advantage of the vegetation in a hospital. From this perspective, the use of vegetation in the hospital needs to scientific and expertise comments by the expert in the green space who is familiar with knowledge of passive defense. In this study, it is tried the principles of architectural green spaces design of medical centers to discuss from an architectural defensive approach and passive defense. For this purpose, using descriptive-analytic method and library research tool, the role of security in outer space of the medical centers are investigated, and in the end, a set of strategies for architectural design of green space for a therapeutic complex in order to reduce the vulnerability of human resources and improve security will be presented, by taking advantage of the fundamental concepts of passive defense. The present research achievements in the designing of green spaces for the medical centers will be useful in line with promoting passive defense purposes.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".