BiomiMETRIC Assistance Tool: A Quantitative Performance Tool for Biomimetic Design
Bibliographic record
Abstract
This article presents BiomiMETRIC, a quantitative performance tool for biomimetic design. This tool is developed as a complement to the standard ISO 18458 Biomimetics-terminology, concepts, and methodology to quantitatively evaluate the biomimetics performance of a design, a project, or a product. BiomiMETRIC is aimed to assist designers, architects, and engineers to facilitate the use of the biomimetic approach beyond the existing frameworks, and to provide an answer to the following question: How can a quantitative evaluation of biomimetic performance be carried out? The biomimetic quantitative performance tool provides a method of quantitative analysis by combining the biomimetic approach with the impact assessment methods used in life-cycle analysis. Biomimetic design is divided into eight steps. The seventh step deals with performance assessment, verifying that the concept developed is consistent with the 10 sustainable ecosystem principles proposed by the Biomimicry Institute. In the application of the biomimetic quantitative performance tool, stone wool and cork are compared as insulation materials used in biomimetic architecture projects to illustrate the relevance and added value of the tool. Although it is bio-based, cork has a lower biomimetic performance according to the indicators used by the biomimetic quantitative performance tool presented in this article.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.010 | 0.024 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.019 | 0.005 |
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".