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 Reuse in systems engineering is a frequent, but poorly understood phenomenon. Nevertheless, it has a significant impact on estimating the appropriate amount of systems engineering effort with models like the Constructive Systems Engineering Cost Model. Practical experience showed that the initial version of COSYSMO, a model based on a “build from the scratch” philosophy, needed to be refined in order to incorporate reuse considerations that fit today's industry environment. The notion of reuse recognizes the effect of legacy system definition in engineering a system and introduces multiple reuse categories for classifying each of the four COSYSMO size drivers – requirements, interfaces, algorithms, and operational scenarios. It fundamentally modifies the counting rules for the COSYSMO size drivers and updates the definition of system size in COSYSMO. In this paper, we present (1) the definition of the COSYSMO reuse extension and the approach employed to define this extension; (2) the updated COSYSMO size driver definitions that are consistent with the reuse model; (3) the method applied to defining the reuse weights used in the modified parametric relationship; (4) a practical implementation example that instantiates the reuse model by an industry organization and the empirical data that provided practical validation of the extended COSYSMO model; and (5) recommendations for organizational implementation and deployment of this extension.
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 | 0.000 | 0.000 |
| 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 | 0.001 |
| Open science | 0.001 | 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