Self-Adapting Resource Bounded Distributed Computations
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
Self-adaptation is about computations adapting to their environments. The need for adaptation may dynamically arise as a result of evolving computations or the environment. An important part of the environment is the computational resources for which computations compete. The CyberOrgs model encapsulates distributed concurrent computations along with the computational and communication resources they require plus purchasing power for acquiring additional resources. Ownership of resources coupled with an effective control mechanism creates a predictable resource environment for computations to execute in - in a coordinated manner. CyberOrgs create three opportunities for self- adaptation: algorithms may be chosen using resource knowledge, additional resources may be purchased to adapt to evolving needs, and computations may coordinate use of known computational and network resources for optimal results. The CyberOrgs model is presented and a prototype implementation is described. Our experience with using CyberOrgs' resource awareness for hierarchical coordination of distributed processor resource delivery is presented. Experimental results show that resource knowledge based reasoning leads to efficient distributed adaptation.
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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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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