Bibliographic record
Abstract
Changelog Added scalable Giga Channels Redesigned the channels in the GUI Partial migration of codebase to Python3 Various market bug fixes Debug pane enhancements: added tunnel/DHT info, statistics Improved unit and integration tests Added VLC 3.0.6 bindings Enable PEX for anonymous sessions Increase min/max_circuits Pony & lz4 added in debian build as pip dependency Fixed TrustChain key usage Fixed errors in SQL upgrade script Fix for DHT not making any requests Renamed lt extension to create_ut_metadata_plugin Update dependencies for Arch Linux Removed DHT retry mechanism Added BEP33 DHT health check Bitcoinlib compatibility with version 0.4.5 Update GUI with discovered channels in real time Fixed multiple instances of Tribler Preventing too long scrape UDP messages Added support for detailed community statistics Remove blocking_call_from_reactor_thread decorators Fixed logging message that still used relay.mid Added check whether resource monitor is enabled Fix GUI glitch in Trust statistics graph Not printing stacktrace when inserting torrent Elided search queries in GUI Fixed exit_nodes file path Fixes in properly handling DHT errors in direct payouts & market community Various fixes for newer bitcoinlib versions Allow code execution, enabled by program flag Fixed race condition in start download dialog Removed bootstrap on circuit removal Added price details to GUI list with asks/bids Colorized peer counts in debug window Added explanation below anonymity slider in GUI Fixed bug when determining category Updated pretty date utility Added support for building snap package Fixed crash when we have a non-empty .unwanted dir Added tracker blacklist file Retry VideoServer port on failure Moved torrent validation to libtorrent Added remote search in Giga Channel Community Deal gracefully with failure to import meliae Free matplotlib memory in Token Mining page Fetching right info key from metainfo Various GUI fixes
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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.008 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.348 | 0.349 |
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".