simpeg/simpeg: Simulation and inversion of time domain IP data
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
Simulation and inversion of time domain IP from pr: #590 commits from: @sgkang, @fourndo, @micmitch review from: @lheagu Implementation: Use stretched exponential (chargeability, time constant, frequency dependency) for parameterization Tested 2D and 3D SIP problems Deprecate <code>Multiregularization</code> and use combo objective function, so pulled <code>ref/objectivefunctions</code> branch - This is for inverting multiple parameters. Thanks @lheagy and @fourndo for combo objective functions, and @rowanc1 for wire implementation! Implement <code>storeJ</code> option for both IP and SIP problems (2D and 3D), which boost up speed for relatively small problems. Example: True model Recovered model Data fit at the fist time channel:
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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