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
Online advertisements (ads) have taken over the web, nowedays most websites contain some sort of ads. While ads produce revenue for the server maintainer or to businesses, they have become intrusive and dangerous as ever. The ads use more bandwidth, show inappropriate content, and spread malware such as adware and ransomware. Although there are many products to block ads, also known as ad blockers, most depend on static filter lists that must be managed manually and frequently updated. When malicious advertisers can produce millions of new URLs within minutes, this is not the most effective method against ads. In this paper we propose our own ad blocker, CIC-AB, which uses machine learning techniques to detect new and unknown ads without needing to update a filter list. The proposed ad blocker has been developed as an extension for the common browsers (e.g. Firefox and Chrome). It classifies URLs, both HTTP and HTTPS, as: non-ad, normal-ad and malicious-ad. The analysis showed the average precision, recall and False Positive rate of CIC-AB for five classifiers namely; Naive Bayes (NB), Support Vector Machine (SVM), K-Nearest Neighbour (KNN), Random Forest (RF) and Decision Tree (DT) is 97.16%, 94.96% and 3.38% respectively.
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 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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