DeepEM: Demonstrating a Deep Learning Approach to DEM Inversion
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
DeepEM is a (supervised) deep learning approach to differential emission measure (DEM) inversion that is currently under development on GitHub. This first release coincides with the version of DeepEM demonstrated in Chapter 4 of the Machine Learning, Statistics, and Data Mining for Heliophysics e-book (Bobra & Mason 2018). Within the chapter (and the code provided here, DeepEM.ipynb) we demonstrate how a simple implementation of supervised learning can be used to reconstruct DEM maps from SDO/AIA data. Caveats of this simple implementation and future work are also discussed. The <em>Machine Learning, Statistics, and Data Mining for Heliophysics </em>e-book can be accessed at https://helioml.github.io/HelioML/, and the interactive DeepEM notebook (Chapter 4) is located at https://helioml.github.io/HelioML/04/1/notebook.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.093 | 0.022 |
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