PROCEEDINGS OF FOURTH BERKELEY CONFERENCE ON DISTRIBUTED DATA MANAGEMENT AND COMPUTER NETWORKS.
Bibliographic record
Abstract
Data base access is increasingly important in a networking environment.Two alternative approaches can be identified: i) implementation of distributed databases presenting the user with one logical database implemented across a collection of computers or, alternatively, ii) development of network data managers providing a uniform user and program viewpoint across heterogenous DBMSs.While the first approach is the most natural extension of the concept of an individual DBMS, its utilization imposes certain requirements including the necessity for converting existing DBMSs if their data is to be supported in the distributed environment.The second approach minimiz.esor eliminates cO"lwersion problems; however, it has not yet caen shown feasible.This paper describes an ongoing research project concerned with establishing the feasibility, issues, alternatives, and a technical approach for supporting a network data manager.Although implementation has not been completed, the initial evidence is positive and suggests that network data managers may well prove either an acceptable alternative or useful intermediate stage to a distributed database.This work Is a contribution of the National Bureau of Standards and Is not subject to copyright.Partial
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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.078 | 0.038 |
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".