Reference System Element Identification Atlas – methods and tools to identify references system elements in product engineering
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
Companies target innovations, successful new products. One major challenge is to increase efficiency and decrease the risk of developing new successful products. We want to reach these goals by improving the reusability of already existing knowledge elements extracted from e.g., already existing (sub-)systems or their documentation. These elements are called reference system elements and are meant to be the starting point for product development projects. Based on a systematic literature review complemented by an expert workshop and analysis of established methods and tools in product engineering, we developed the Reference System Elements Identification Atlas to support the identification of suiting reference system elements. Within the Reference System Elements Identification Atlas, we collected 30 methods and tools to identify reference system elements and allocated them to the various knowledge spaces they search. All 30 methods and tools were grouped in five clusters – creativity methods, data analysis methods, market/competition analysis methods, similarity methods, and trend analysis methods. We observed that methods and tools are hardly related to the identification of reference system elements in literature explicitly. We believe the Reference System Elements Identification Atlas provides valuable support to collect valuable reference system elements as the starting point in product engineering. • Systematic literature review on methods and tools to identify existing knowledge. • Match of the methods and tools with diverse knowledge spaces to search in. • Knowledge management by means of knowledge reuse.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.005 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it