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
Cluster based replication solutions are an attractive mechanism to provide both high-availability and scalability for the database backend within the multi-tier information systems of service-oriented businesses. An important issue that has not yet received sufficient attention is how database replicas that have failed can be reintegrated into the system or how completely new replicas can be added in order to increase the capacity of the system. Ideally, recovery takes place online, i.e, while transaction processing continues at the replicas that are already running. In this paper we present a complete online recovery solution for database clusters. One important issue is to find an efficient way to transfer the data the joining replica needs. In this paper, we present two data transfer strategies. The first transfers the latest copy of each data item, the second transfers the updates a rejoining replica has missed during its downtime. A second challenge is to coordinate this transfer with ongoing transaction processing such that the joining node does not miss any updates. We present a coordination protocol that can be used with Postgres-R, a replication tool which uses a group communication system for replica control. We have implemented and compared our transfer solutions against a set of parameters, and present heuristics which allow an automatic selection of the optimal strategy for a given configuration.
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 | 0.000 | 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