BayesFit: A tool for modeling psychophysical data using Bayesian inference
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
BayesFit is a module for Python that allows users to fit models to psychophysical data using Bayesian inference. The module aims to make it easier to develop probabilistic models for psychophysical data in Python by providing users with a simple API that streamlines the process of defining psychophysical models, obtaining fits, extracting outputs, and visualizing fitted models. Our software implementation uses numerical integration as the primary tool to fit models, which avoids the complications that arise in using Markov Chain Monte Carlo (MCMC) methods [1]. The source code for BayesFit is available at https://github.com/slugocm/bayesfit and API documentation at http://www.slugocm.ca/bayesfit/. This module is extensible, and many of the functions primarily rely on Numpy [2] and therefore can be reused as newer versions of Python are developed to ensure researchers always have a tool available to ease the process of fitting models to psychophysical data.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it