Bibliographic record
Abstract
The rapid growth of the World Wide Web has complicated the process of Web browsing by providing an overwhelming wealth of choices for the end user. To alleviate this burden, intelligent tools can do much of the drudge-work of looking ahead, searching and performing a preliminary evaluation of the end pages on the user’s behalf, anticipating the user’s needs and providing the user with more information with which to make fewer, more informed decisions. However, to accomplish this task, the tools need some form of representation of the interests of the user. This article describes the SWAMI system: SWAMI stands for Searching the Web with Agents having Mobility and Intelligence. SWAMI is a prototype that uses a multi-agent system to represent the interests of a user dynamically, and take advantage of the active nature of agents to provide a platform for look-ahead evaluation, page searching, and link swapping. The collection of agents is organized hierarchically according to the apparent interests of the user, which are discovered on-the-fly through multistage clustering. Results from initial testing show that such a system is able to follow the multiple changing interests of a user accurately, and that it is capable of acting fruitfully on these interests to provide a user with useful navigational suggestions.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.228 | 0.142 |
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".