Bibliographic record
Abstract
PINT: Maximum-Likelihood Estimation of Pulsar Timing Noise Parameters PINT is a project to develop a pulsar timing solution based on python and modern libraries. It is still in active development, but it is in production use by the NANOGrav collaboration and it has been demonstrated produce residuals from most "normal" timing models that agree with Tempo and Tempo2 to within ~10 nanoseconds. It can be used within python scripts or notebooks, and there are several command line tools that come with it. The primary reasons PINT was developed are: To have a robust system to produce high-precision timing results that is completely independent of TEMPO and Tempo2 To make a system that is easy to extend and modify due to a good design and the use of a modern programming language, techniques, and libraries. What's Changed Fixed README for runtime data by @dlakaplan in #1685 Added convert_parfile to list of command-line tools in RTD by @dlakaplan in #1687 Fix MCMC_walkthrough.ipynb by @dlakaplan in #1679 Fixed bug in derived params when OMDOT uncertainty is 0 by @dlakaplan in #1689 Fix bug in make_fake_toas_fromtim by @dlakaplan in #1698 Fix observatory.init.py by @dlakaplan in #1696 Plot model DM in pintk + more tests for pintk by @abhisrkckl in #1678 Moved get_derived_params to timing_model by @dlakaplan in #1692 DDH Binary model by @dlakaplan in #1693 Maximum-likelihood fit for ECORR by @abhisrkckl in #1673 Consistent naming in get_params_mapping by @abhisrkckl in #1704 Better exceptions for unimplemented binary models (BTX etc) by @abhisrkckl in #1701 CI ephemeris test no longer requires access to static NANOGrav site by @dlakaplan in #1706 change_binary_epoch in TimingModel.compare() by @abhisrkckl in #1703 Component validation for WaveX and DMWaveX by @abhisrkckl in #1702 get_observatory shouldn't overwrite include_bipm and include_gps unless given explicitly by @abhisrkckl in #1711 Convert WaveX to PLRedNoise and DMWaveX to PLDMNoise by @abhisrkckl in #1694 Added spacecraft as alias for geocenter by @dlakaplan in #1724 Allow simulated TOAs to maintain a non-zero mean by @dlakaplan in #1717 Improve clock error reporting by @aarchiba in #1720 Allow very old Princeton format TOAs by @scottransom in #1722 Remove ERFA warnings by @aarchiba in #1721 Removed np.compat.long (deprecated) by @dlakaplan in #1728 Fix #1729 by @dlakaplan in #1730 Changed error bars to scaled for pintk by @dlakaplan in #1735 Fixed common CI failure in tests/test_observatory.py::test_json_observatory_input_latlon by @dlakaplan in #1738 Updated docs for ELL1H by @dlakaplan in #1736 FDJUMPDM : System-dependent DM offset by @abhisrkckl in #1731 Proper motion conversion/calculations can now uniformly use float, Quantity, or Time by @dlakaplan in #1737 Try macos 12 to see if it is still intel by @dlakaplan in #1745 Guess binary model & add conversion script by @vhaasteren in #1695 Full Changelog: 0.9.8...1.0
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.007 | 0.003 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.182 | 0.171 |
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".