Classification, Characterization and Abstraction of Vegetable Surfaces for Design
Bibliographic record
Abstract
Biological design tools such as biomimicry and bio-inspiration are increasingly used resources to solve design and engineering problems, owing to their high probability of finding efficient technical solutions and novel morphologies with the potential to positively impact the environment.This article focuses on the development of a methodology for the morphological characterization of plant surfaces, which can be applied in a conventional design or research project based on biological information.The process proposed here is part of the results of the research project 'Repertory of bio-inspired surfaces and textures, through morphological experiments with Digital Manufacturing Technologies'.For the development of the characterization, the observation and selection of 225 species, which were grouped and categorized based on the predominant geometric characteristics, resulting in six categories: basic geometries, reciprocals, reliefs, ramifications, folds and visual texture.Then, the natural models were evaluated qualitatively and the representative samples of each category to later perform the process of digital abstraction using CAD and parametric tools.In the process of abstraction a description and visual analysis is made from: transition points, contours, symmetries, cross-sections and basic patterns as straight lines, sections of circle, circumferences, ellipses and/or parabolas.It was possible to verify that the three-dimensional interpretation that was made digitally of the textures, preserve the fundamental morphological properties, in addition to, to keep a direct relationship with the category to which they belong, achieving a balance between the faithful copy and the abstract copy; this in order to maintain the formal properties with potential to be applied in future design projects.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".