Resource Warehouses; a Distributed Information Warehouse Infrastructure
Bibliographic record
Abstract
This paper presents work related to the design of distributed systems, which is useful for emerging Internet applications. We propose algorithms for searching and managing distributed information about resources and services using locally available warehouses. The concept of warehouses has been introduced in the Web Operating System (WOS) (Kropf 1999). Warehouses have the ability to decide which information should be stored, replaced or removed without any intervention of the user. We present a tree structure for WOS warehouses, an attribute/value scheme used for describing resources, and the algorithms to look up information about resources. Among other things, warehouses take into account the capacity limitations of the devices that the WOS is using. Moreover, in order to share locally available information, WOS warehouses need to communicate with each other. We present an approach which allows for profitable exchange of information between the various warehouses. The advantage of our ap- proach is the use of a simple method to describe what is be- ing looked for (i.e., the intent), instead of specifying where to find it (i.e., the extent). We have implemented our warehouse structure in Java taking advantage of its portability.
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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.005 |
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".