OnlyCCFA

Filters Google Scholar and Semantic Scholar, exports reliable BibTeX, and keeps noisy results out.

OnlyCCFA is a Google Scholar deep-filtering and venue-rank extension for researchers, students, and engineers. It is not just a rank-label overlay: it turns Google Scholar into a practical workflow for filtering papers, checking venue signals, exporting citations, and preparing a cleaner candidate set for Zotero. OnlyCCFA shows CCF recommended ranks on Google Scholar, dblp, Connected Papers, Semantic Scholar, and Web of Science. On Google Scholar, it focuses on CCF-A results by default, while still letting you switch between ALL, CCF-A, CCF-B, and CCF-C at any time. Key features: - Default CCF-A filtering on Google Scholar, with quick ALL / CCF-A / CCF-B / CCF-C switching - Deep-scan multiple Google Scholar pages and continue loading the next result batch - More than 22,000 JCR 2024 / CAS upgraded partition 2025 journal records, plus a fix so DBLP fallback results can show JCR/CAS badges instead of only CCF - Google Scholar profile-page support for publication-table badges, combined filters, single-paper BibTeX copy, and batch export - Multi-source badges for SCI, JCR Q1/Q2, CAS partitions/TOP, EI, Chinese core journals, NSFC Distinguished Young Scholar seed names, official CAS/CAE Chinese+English academician lists, SWJTU lists, and field TOP venues for robotics, communications, electrical engineering, control, and mechanical engineering - Combine badges with “any” or “all” logic to reduce noisy search results quickly - Make Zotero Connector see only the currently filtered Google Scholar results by synchronizing the filtered DOM - Export reliable BibTeX from Google Scholar first, with Crossref / arXiv public metadata fallback when needed - Faster batch BibTeX export with limited concurrency, result caching, and readable multi-line formatting - Keep visual badges outside the paper title, so Zotero imports clean titles without CCF/JCR/SCI labels appended - Save local preferences for language, default filter, deep-filter size, and selected badges OnlyCCFA is designed for students and researchers in computer science, robotics, mechanical engineering, electrical engineering, communications, and related fields. The goal is to make venue-quality signals more transparent, free, and usable directly inside everyday academic search. Privacy: OnlyCCFA does not collect personally identifiable information, does not sell data, and does not upload browsing history to the developer’s server. Preferences and caches stay in the browser. Public academic metadata requests are made only when the user explicitly starts deep filtering or exports BibTeX.