Analysis of gravity gradiometer inverse problems using optimal design measures
Why this work is in the frame
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Bibliographic record
Abstract
ABSTRACT Tensor components provide related but independent measures of the gravity field. In commercial systems, some of the components are measured directly while others are calculated. With five independent tensor components available for interpretation, the question of which components to use arises. Using ideas from optimal survey design, we investigated the information content provided by each of the tensor components and combinations thereof. The main tool used is the singular value decomposition of the design matrix resulting from posing the 3D inversion problem of determining densities from gravity gradiometer data. Optimal design measures suggest that at larger measurement-source distances all components and combinations provide similar levels of information concerning the source density distribution. Simply adding more components to the inverted data set does not provide any advantages. This changes at smaller measurement-source distances where multicomponent combinations provide stronger information content and Tzz is the single tensor component with the largest information value.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.003 |
| 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