Managing combinatorial design challenges using flexibility and pathfinding algorithms
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 Morphological matrices (MMs) have traditionally been used to generate concepts by combining different means. However, exploring the vast design space resulting from the combinatorial explosion of large MMs is challenging. Additionally, all alternative means are not necessarily compatible with each other. At the same time, for a system to achieve long-term success, it is necessary for it to be flexible such that it can easily be changed. Attaining high system flexibility necessitates an elevated compatibility with alternative means of achieving system functions, which further complicates the design space exploration process. To that end, we present an approach that we refer to as multi-objective technology assortment combinatorics. It uses a shortest-path algorithm to rapidly converge to a set of promising design candidates. While this approach can take flexibility into account, it can also consider other quantifiable objectives such as the cost and performance of the system. The efficiency of this approach is demonstrated with a case study from the automotive industry.
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.
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 | 0.001 | 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 it