CIRA-Pulsars-and-Transients-Group/vcstools: FEE2016 Beamformer
Bibliographic record
Abstract
Upgrades: Implementation of the FEE2016 beam model version for the beamformer Added the option to choose which beam model to use in the RTS. The default is the FEE2016 model Beamformer is now run natively instead of using the singularity image on Garrawarla Changes to rm_synthesis.py's plot range and title Fixes: Fixed Zeus copyq jobs so they work when launched from Garrawarla Added export UCX_MEMTYPE_CACHE=n to RTS jobs so they work on Garrawarla Removed unnecessary module loads in the recombine job that were causing errors on Garrwarala Fixed a metadata check as the 'metadata' key no longer exists Added the -V option to the scripts that didn't have it Updated the config and scripts to have computer dependant group ids for directories they create Raises an exception if no pulsars found in catalogue with set parameters Correlator now loads vcstools if using it --offline tag. Added option to choose vcstools version to load Updated the metadata calls that get files information as Andrew Williams wants to remove the files from the standard metadata call (makes the call too large) and instead has a separate 'data_files' call Fixed a bug in rts2ao.py by uncommenting a command. Added some debugging and exception handling
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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.295 | 0.235 |
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".