Review of the hemp-based brick that purifies air pollution, reduces humidity and is a thermal insulator: An innovative solution to reduce environmental pollution in the construction industry
Bibliographic record
Abstract
In the construction industry there are several problems that negatively impact the environment from the process of obtaining materials, the construction of buildings and their use. That is why the bibliographic review research analyzes the use and application of the hemp-based brick with the support of the Scopus database and the VOSviewer software as data analytics, the characteristics of the brick were taken into account as well As an air pollution purifier, humidity reduction and thermal insulation, graphs related to these characteristics were obtained, highlighting the words with the highest frequency in the research, countries that published and bibliometric maps worldwide. The results help us determine that there is an application of this brick in countries with the greatest needs and growth and also in potential countries, however we believe that in Peru there is no analysis or interest in this brick. This hemp-based brick has positive advantages for the environment and is potentially ecological from the beginning of obtaining the materials, used in the construction of houses and then as waste, in these three phases it contributes to the environment. The purpose of the research is to be the beginning for future investments and research related to the hemp-based brick applied in the construction industry and the other objective is to motivate professionals to use and apply this brick that will have benefits for the environment.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.011 | 0.015 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".