dodge-research-group/thztools: THzTools v0.4.0 (alpha)
Bibliographic record
Abstract
Changed Rename module constant global_options as options Rename tdnoisefit as noisefit Rename NoiseModel attributes alpha, beta and tau as sigma_alpha, sigma_beta and sigma_tau, respectively Rename NoiseModel methods amplitude, noise, and variance as noise_amp, noise_sim, and noise_var, respectively Revise noisefit and associated functions to treat each of the 3 noise parameters separately instead of as a 3-component array, so that each may fixed independently in the optimization process Adjust internal scaling in noisefit Reorganize NoiseResult class as output for noisefit Revise NoiseResult docstring Rename transfer_out as transfer Rename fft_sign parameter as numpy_sign_convention in transfer Make dt and t0 keyword-only arguments in wave and set defaults Change position of dt in scaleshift, tdnoisefit, transfer docstrings and change defaults Revise defaults for wave Change default value for dt in fit Improve type annotations Update tests Update dependencies in environment-dev.yml and pyproject.toml Revise Getting Started page Revise documentation, including Sphinx format and project-specific layout Added Add R. P. Hall, Laleh Mohtashemi and Naod Yimam to the author list Add missing ORCID IDs Add get_option and set_option functions to handle global options Add attributes to NoiseResult class Add timebase function Add seed parameter in NoiseModel.noise_sim Add NoiseResult class to documentation Add doctests Add warnings Add paper directory with bib-file and stub for JOSS paper Add draft-pdf.yml GitHub Action to autogenerate JOSS paper; update deprecated upload-artifact Action in workflow Add Contributing and Examples pages to documentation Add Code of Conduct Add gallery of Jupyter notebook examples to documentation using nbsphinx Fixed Fix error in units of NoiseModel.sigma_tau in noisefit Fix bug related to eta scaling in noisefit
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.003 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.071 | 0.177 |
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; both teacher heads agree on what is shown here.
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".