Bibliographic record
Abstract
Selecting commercial-off-the-shelf (COTS) products is a challenging process that utilizes and generates a lot of information. Repositories play a crucial role in the management of the COTS selection information. In fact, it is generally believed in literature that repositories are of great importance to the COTS selection process and indeed to the entire process of developing software using COTS products. However, the process of developing, managing, and accessing these repositories has attracted very little attention. This paper presents a framework for establishing and maintaining the following five different repositories for the COTS selection process: COTS repository, user repository, discussions repository, lessons-learned repository, and historical information repository. The framework supports distributed contribution and access to the repositories, as well as systematic and hierarchical evaluation and integration of the contributions. Moreover, this paper presents a description of a database that was implemented as part of a decision support system (DSS) for the selection of COTS products. The database accommodates the different repositories for the COTS selection process
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.013 | 0.015 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.004 |
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 source (direct Gemma or distilled Codex), 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".