What do we mean by sensitivity analysis? The need for comprehensive characterization of “global” sensitivity in <scp>E</scp>arth and <scp>E</scp>nvironmental systems models
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
Abstract Sensitivity analysis is an essential paradigm in Earth and Environmental Systems modeling. However, the term “sensitivity” has a clear definition, based in partial derivatives, only when specified locally around a particular point (e.g., optimal solution) in the problem space. Accordingly, no unique definition exists for “global sensitivity” across the problem space, when considering one or more model responses to different factors such as model parameters or forcings. A variety of approaches have been proposed for global sensitivity analysis, based on different philosophies and theories, and each of these formally characterizes a different “intuitive” understanding of sensitivity. These approaches focus on different properties of the model response at a fundamental level and may therefore lead to different (even conflicting) conclusions about the underlying sensitivities. Here we revisit the theoretical basis for sensitivity analysis, summarize and critically evaluate existing approaches in the literature, and demonstrate their flaws and shortcomings through conceptual examples. We also demonstrate the difficulty involved in interpreting “global” interaction effects, which may undermine the value of existing interpretive approaches. With this background, we identify several important properties of response surfaces that are associated with the understanding and interpretation of sensitivities in the context of Earth and Environmental System models. Finally, we highlight the need for a new, comprehensive framework for sensitivity analysis that effectively characterizes all of the important sensitivity‐related properties of model response surfaces.
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 | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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