BIKAS: Bio-Inspired Knowledge Acquisition and Simulacrum—A Knowledge Database to Support Multifunctional Design Concept Generation
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
ABSTRACT A detailed acquisition, analysis, and representation of biological systems exhibiting different functions is required to develop unique bio-inspired multifunctional conceptual designs and methods. This paper presents BIKAS: Bio-inspired Knowledge Acquisition and Simulacrum, a knowledge database of biological systems exhibiting various functionalities, developed based on case-based bio-inspired examples from literature. The knowledge database represents the biological features, their characteristics, and the function exhibited by the biological feature as a combination of its integrated structure and structural strategy. Furthermore, this knowledge database is utilized by the Expandable Domain Integrated Design (xDID) model that works on classifying, mapping, and representing biological features into their respective geometric designations called Domains. The combination of features from the Domains results in the generation of multifunctional conceptual designs. In addition, Meta-level design factors are proposed to aid designers in filtering the biological features and their respective functions having a similar structural strategy, thus aiding designers in rapidly selecting and emulating biological functions.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it