Bibliographic record
Abstract
This report examines selected aspects of autonomic computing, explores some of its strengths and weaknesses, and outlines some of the current research projects being undertaken in this area. It also makes connections between this area and current work in several initiatives at the Carnegie-Mellon (trademark) Software Engineering Institute (SEI), namely the Predictable Assembly from Certifiable Components and Software Architecture Technology (SAT) Initiatives. Several pieces of work being undertaken in these initiatives have connections to autonomic computing. Furthermore, the report describes the potential and impact of autonomic computing for Department of Defense (DoD) systems, and outlines some of the challenges for the DoD as it moves to exploit autonomic computing technology. The following autonomic computing projects are profiled: Unity Project and Autonomic Computing Toolkit; Information Economies Project; KX Project (Columbia University); Rainbow Project (Carnegie-Mellon University); ROC Project (University of California Berkeley/Stanford); DEAS Project (Universities of Victoria and Toronto, Canada); Autonomia Project (University of Arizona); AutoMate Project (Rutgers University); AMUSE Project (University of Glasgow and Imperial College, UK); Astrolabe Project (Cornell University). At the end of the report is an extensive bibliography, which shows how quickly the autonomic computing field has grown since 2001. The bibliography contains a number of documents that would be good starting points for newcomers to the field of autonomic computing.
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 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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".