An efficient dual caching strategy for web service-enabled PDAs
Bibliographic record
Abstract
PDAs have evolved over the years from resource constrained devices that supported only the most basic tasks to powerful handheld computing devices. However, the most significant step in the evolution of PDAs was the introduction of wireless connectivity which enabled them to host applications that require internet connectivity like email, web browsers and maybe most importantly smart/rich clients. Being able to host smart clients allows the users of PDAs to seamlessly access the IT resources (e.g. legacy apps) of their organizations. One increasingly popular way of enabling access to IT resources is by using Web Services (WS) [14]. This trend has been aided by the rapid availability of Web Service (WS) packages/tools, most notably the efforts of the Apache group [1] and IDE vendors (e.g., Microsoft's Visual Studio [2], IBM's Eclipse [3]). Using IDE tools and other software packages it is fairly easy for programmers to expose application interfaces and/or consume existing interfaces leading to a gradual replacement of the current web server centric approaches (e.g. ASP, JSP, Servlets, CGI scripts) with WS centric approach.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".