Power Consumption While Using Ad-Blockers on ARM-Based CPU
Bibliographic record
Abstract
This study investigates the impact of ad blockers on power consumption in ARM-based processors, which are widely used in energy-efficient systems. A comparative analysis was conducted across popular browsers such as Chrome, Brave, Vivaldi, Kiwi, and Firefox, alongside ad blockers including AdGuard, Adblock Plus, Ghostery, uBlock, and uBlock Origin. Tests on websites like YouTube, Dailymotion, ARYZAP, and KissCartoon revealed significant differences in power consumption based on browser and ad-blocker configurations. Kiwi paired with uBlock reduced power consumption by approximately 15% compared to Chrome, which consistently exhibited the highest energy usage. Brave, with its built-in ad blocker, reduced power consumption by 12% on average compared to Firefox with Ghostery, which showed the highest consumption. Additionally, Firefox with Adblock Plus demonstrated an 8-10% reduction in energy use compared to configurations without ad-blocking extensions. On media-rich platforms like YouTube, Brave and Kiwi performed more efficiently, consuming 10-13% less power than Chrome and Firefox with Ghostery, which increased energy use by up to 20%. These findings emphasize the importance of selecting the right browser and ad blocker combination to optimize power efficiency on ARM-based systems, especially in ad-heavy environments.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".