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
Many large-scale utility computing infrastructures comprise heterogeneous hardware and software resources. This raises the need for scalable resource selection services that identify resources that match application requirements. Such a service must provide an efficient lookup in spite of changing resource attributes such as disk size, changing application requirements such as installed software libraries, and changing system composition as resources join or leave. We present a fully decentralized, self-managing Resource Selection Service (RSS) algorithm by which resources autonomously select themselves when their attributes match a query. An application specifies what it expects from a resource by means of a conjunction of (attribute,value-range) pairs, which are matched against the attribute values of resources. The set of search attributes can also be updated online to reflect new requirements. We show that our solution scales in the number of resources and in the number of attributes, while being relatively insensitive to churn and other membership changes like node failures. Our RSS continuously self-adapts its routing structure in response to variations in the distribution of node attributes and queries. We show that this autonomous optimization maintains performance and availability in a long-lived service even when the set of application requirements used to select resources changes.
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 | 0.001 |
| Science and technology studies | 0.001 | 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