NON-CONVENTIONAL BUILDING TECHNOLOGIES AS A PANACEAAGAINST THE COVID-19 PANDEMIC
Bibliographic record
Abstract
The impact of the COVID-19 pandemic has shown the lack of adequate housing and infrastructures around the world.The crisis has brought to light the fact that many countries do not have the means to offer adequate treatment medical centres for their population promptly.According to the World Health Organisation (WHO), a COVID patient must be appropriately quarantined or isolated for treatment and this requires a comfortable and safe space to facilitate the speedy recovery of the patient.However, most of the existing hospitals and clinics are built with concrete, timber, and steel (generally referred to as conventional building technologies), which take more time and sometimes costly to construct.Fortunately, other methods can speed up the construction process and also offer an improved environment for the patient and other users in comparison to the conventional building technologies.One such method is known as the non-conventional building technology, also known under various nomenclatures such as Modular Building Systems (MBS), Alternative Building Technologies (ABTs), and Innovative Building Technologies (IBTs).These technologies are available on the market and are generally referred to as Green Building (GB) products.In addition to being environmentally friendly, GB also promotes sustainability and can be used to reduce the lack of housing stock and infrastructure in the community.This article reviews nonconventional building technologies presented by many authors.The adoption of the non-conventional building technologies differs from one country to another, with each country having its standards and procedures to approve the products.As in any technology, there are advantages and disadvantages, but this paper shows that the use of non-conventional building technologies can be used as a panacea to fight against the impact of the COVID -19 crises.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".