Bibliographic record
Abstract
This is the sevententh production release of PyCBC for use in LIGO's second observing run. This release has been tested against LALSuite with the hash: 539c8700af92eb6dd00e0e91b9dbaf5bae51f004 This release fixes two packaging bugs: That PyPi distribution of v1.7.6 was missing a file which causes installation of PyCBC using pip to fail. Release v1.7.7 was incorrectly tagged causing the OSG bundled executable build to fail. The v1.7.6 release has been removed from PyPi. The v1.7.7 release has been removed from PyPi, CVMFS and Docker. There are no other substantial code changes in this release. Details of the changes since the last production release are at https://github.com/ligo-cbc/pycbc/compare/v1.7.5...v1.7.8 A Docker container for this release is available from the pycbc/pycbc-el7 repository on Docker Hub be downloaded using the command: docker pull pycbc/pycbc-el7:v1.7.8 On a machine with CVMFS installed, a pre-built virtual environment is available for Red Hat 7 compatible operating systems by running the command: source /cvmfs/oasis.opensciencegrid.org/ligo/sw/pycbc/x86_64_rhel_7/virtualenv/pycbc-v1.7.8/bin/activate and for Debian 8 compatible operating systems by running the command: source /cvmfs/oasis.opensciencegrid.org/ligo/sw/pycbc/x86_64_deb_8/virtualenv/pycbc-v1.7.8/bin/activate A bundled pycbc_inspiral executable for use on the Open Science Grid is available at /cvmfs/oasis.opensciencegrid.org/ligo/sw/pycbc/x86_64_rhel_6/bundle/v1.7.8/pycbc_inspiral
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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.265 | 0.264 |
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".