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
Sensitivity analysis is a mathematical technique for investigating the effects of inaccuracies in the parameters of a mathematical model. It analyses how variation in the output of a model (numerical or otherwise) can be apportioned qualitatively or quantitatively to different sources of data. Sensitivity analysis is an important statistical validation technique in Bayesian Modelling. It is used to ascertain how a given model output depends upon or determines its input parameters. It often carried out to ensure the quality and accuracy of a model and a way of checking the robustness and reliability of assumptions built into a model. This Chapter offers an accessible introduction to sensitivity analysis of Bayesian models. The Chapter should have been a section in Chapter 12 and presented before the scenarios sections in Chapter 12. But because sensitivity analysis itself is a complex subject, it was deemed wise to present it as a complete Chapter on its own.
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.
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 | 0.000 | 0.000 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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