Customizing and delivering mobile services using software agents and CC/PP
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
A downside brought about by the explosive growth of online information and the shear amount of data traffic moving through our networks is the modern day headaches of information overload. The growth of handheld wireless devices can only mean that the end-users will continually be bombarded by emails and real-time information feeds, but now, even as they are on the go. Software agents provide mechanisms that have good potentials to lower the amount of information that an end- user has to deal with. Agent-enabled applications allow the end- user to personalize the way online resources are presented and can also filter out irrelevant or unwanted information. In this paper, we present our experience in using software agents and the Composite Capabilities/Preference Profiles (CC/PP) for customizing and delivering mobile services for Java 2 Micro Edition (J2ME) enabled and Wireless Application Protocol (WAP) enabled devices. We use software agents because such autonomous software entities have characteristics that can benefit mobile devices and the wireless environment, and the CC/PP is a standard for defining profiles for user preferences and device capabilities.
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 | 0.001 |
| 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