Bibliographic record
Abstract
Consider a geographic information system (GIS) which is set up as a Web server that allows users to query the database with a Web browser. As the query result may be huge and the network delay could be significant, we investigate the fundamental problem of how to deliver the query result efficiently over the network . In a conventional client-server database system, the commonly used application programming interface (API) is the so-called iterator-based interface in which a client queries the server with an ISQL statement and the result, which is called a result or active set, is generated. To retrieve the query result, multiple calls are made to the server and objects in the result set are retrieved sequentially. To enhance system performance, objects in a result set can also be retrieved in bulk by storing them in an array. In the Web environment, a database server is commonly implemented with a distributed object technology such as Java or CORBA. As network delay could be significant and the client memory spaces are limited and varying, neither multiple calls nor bulk-retrieval is a viable solution to this problem. We propose a technique by caching and piping the result set through a socket connection without forfeiting the iterator-based interface. We show that the proposed method is superior in delivering a query result in a LAN and in the Web environment. We then investigate how to retrieve and display geometric data in a map efficiently in a network environment.
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".