THE USE OF BIONICS AND SEMANTIC PANEL FOR A PRODUCT DEVELOPMENT
Bibliographic record
Abstract
The essence of the designers work is the creation of new solutions, usually related to the project of products to be produced in large scale. Thus, design is explicit in manufacturing companies but it is not usually related to raw material industries. However, designers work is necessary to materialize intangible products, as chemicals inputs, into concrete concepts. This is not as easy as it could seem and may demands special methods, as semantic panel. The semantic panel is a technique based on communication through metaphors. This can stimulate creativity allowing the designer to combine things that belong to different contexts and formulate new solutions to project from these associations. This paper describes the method and the steps performed for the development of a product that aims to turn tangible a new chemical solution with low environmental impact developed by an Italian chemical company. The method was conducted in two steps. First, an analysis of the design problem was performed by the designer and the company’s technicians and executives. Secondly, a semantic panel was constructed under the theoretical basis of bionics to define practical, symbolic, aesthetic and green product functions. The bionic analysis of a groundling plant resulted in the detection of parameters that could be suitable for the product design, as a good balance, physical and mechanical strength. The concept, plus the problem presented and the semantic panel led to the requirements of the product aesthetics (color and texture), symbolism (meaning) and ecological factors (processes of tanning the leather with chemical inputs). The resulting product from this procedure; a purse, achieved the project goals expressing clearly the performance expected from the chemical input.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".