Bibliographic record
Abstract
Breaking change: scsynth had a security issue where it listens to 0.0.0.0 by default. For most users, this is undesirable behavior since it allows anyone on your local network to send messages to scsynth! This default has been changed to 127.0.0.1 (#4516). To change it back (e.g. for networked server/client setups), use -B 0.0.0.0 at the command line or server.options.bindAddress = "0.0.0.0". On Windows, scsynth was not able to select separate input and output devices. Since many audio drivers present inputs and outputs as separate devices, this caused major blocking issues for anyone using Windows with an external sound card. This has been fixed (#4475). Fixed crashes trying to run multiple IDEs at once, and a related error when attempting to run HelpBrowser:instance in sclang while an IDE help browser is open (#4267). Fix issues when using a regular Buffer (that is, not a LocalBuf) for FFT (#4050). Fixed class library compilation issues on Qt-less sclang installations (#4219). On macOS, Cmd+Q in the IDE would quit the interpeter but not the IDE. This is a regression from old behavior where the IDE was quit entirely. This has been fixed (#4300). Since 3.10, the help browser would execute code twice when selected. This has been fixed (#4390).
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 | 0.008 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.007 | 0.006 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.632 | 0.650 |
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".