THzTools: data analysis software for terahertztime-domain spectroscopy
Bibliographic record
Abstract
Changed Change default noisefit algorithm to estimate mu using a weighted average of the measured waveforms after rescaling and shifting, rather than optimizing the cost function with respect to mu directly (#85) (Alireza Noori) Change procedure to evaluate parameter uncertainties, from direct computation of the Hessian to numerical evaluation using numdifftools (#85) (Alireza Noori) Deprecate mu_err and p_err attributes of FitResult class (#108) (Alireza Noori) Added Add etfe function (Augusten Fairbairn, Japneet Sandhu, Chloe Chan) Add workers parameter to noisefit function to allow parallel processing of FFTs (#78) (Rabin Meetarbhan) Add links to top keywords in documentation (Wesley Xu) Add est_mu parameter to thztools (Alireza Noori) Add support for Python 3.14 (#92) (Steve Dodge) Add mu_cov, psi_cov, delta_norm, delta_norm_cov, epsilon_norm, epsilon_norm_cov, and delta_norm_epsilon_norm_cov attributes to FitResult class (#108) (Alireza Noori) Removed Remove support for Python 3.9 (#92) (Steve Dodge) Fixed Fix noisefit examples in documentation (#81) (Alireza Noori) Apply Bessel correction to estimated noise model parameter uncertainties returned by noisefit (#86) (Alireza Noori) Fix error in cost function Jacobian used in noisefit (#87)) (Alireza Noori) Fix erroneous warnings when using NumPy <2.3 on Apple Silicon M4 chips numpy/numpy#28687 Fix error in cost function Jacobian when numpy_sign_convention = False (7c11c58) (Steve Dodge) Fix inconsistent internal scaling of eta parameter (c0a725f)(Steve Dodge)
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 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.087 | 0.023 |
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".