Bibliographic record
Abstract
Mobile web browsing is one of the most commonly used and wide-spread application (app) among smartphone apps. However, the complexity of web-pages is increasing especially if the web-pages are designed for desktop computers. The existence of advertisements (ads) in web-page leads to even higher complexity of the web-pages. This complexity in smartphone's environment where the resources are limited (e.g., battery and bandwidth) is reflected in longer loading time, more energy consumed, and more bytes transferred. In this paper, we classify the web contents into: (i) core information, and (ii) forced "unwanted" information, namely ads. Then, we evaluate resources used for web advertising. Based on the measurements of the cost of web advertising, we propose a framework for mobile browsing that adapts the web-pages delivered to the smartphone, based on the smartphone's current battery level and the network type. The adaptation of the web content is in the form of controlling the amount of ads to be displayed on the web-page. Our system aims to (i) extend smartphone battery life and (ii) preserve the bandwidth needed to download the web-pages while balancing the satisfaction of the publishers of web-pages as well as the end users.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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".