A Review of Biological Studies and their Relation with Environmental Sustainability
Bibliographic record
Abstract
Today, human activities are the leading cause of severe environmental damages, especially regarding shortage and instability of natural resources. The sustainability concept was developed in response, to limit these risks and facilitate living conditions for future generations. Life and nature have historically been considered as inspiring in architecture among others, prompting an environmental-oriented sense of direction towards resolving present-day issues. Despite the proliferation of nature-inspired sciences, hardly any attention is paid to classifications and interconnections between these sciences regardless of benefits associated with such studies. However, the availability of such a classification and its use in present-day architecture can significantly contribute to the advancement of environmental sustainability.The current research has identified and presented a comprehensive framework of sciences inspired by life and nature (bio) and their relationship. The research method is qualitative-descriptive and focuses on articles containing the word ‘bio’, published in Springer publishers between 2020 and 2022. The review of the 1927 articles have led to a classification of biological studies into 11 groups (bionic, biomimetic, biomorphic, biofuel (biodiesel, bioalcohol, bioethanol, biohydrogen, and biogas), biomaterial, biohybrid, bioaerosol, biotechnology, bioalgorithm (biodesign intelligence and biomaterial intelligence), bioart (bio-inspired design and biodecoration), biophilia (biophilic design) and the relationship between them environmental sustainability. The outcome is a development of the theoretical knowledge about bio-inspired sciences and indicates that international bioscience studies mainly focus on biofuels and biotechnology, along the lines of reducing human activities’ impact on the environment, efficient use of natural resources, protect biodiversity, and a harmonious relationship between humans and the natural 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.011 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".