Bibliographic record
Abstract
ABSTRACT Gravity gradiometry allows individual components and combinations of components to be used in interpretation. Knowledge of the information content of different components and their combinations is therefore crucial to their effectiveness, and a quantitative rating of information level is needed to guide the choice. To this end, I use linear inverse theory to examine the relationship between the different tensor components and combinations thereof and the model parameters to be determined. The model used is a rectangular prism, characterized by seven parameters: the prism location xc, yc; its width w and breadth b; the density ρ; the depth to top z; and thickness t. Varying these values allows a variety of body shapes, e.g., blocks, plates, dykes, and rods, to be considered. The Jacobian matrix, which relates parameters and their associated gravity response, clarifies the importance and stability of model parameters in the presence of data errors. In general, for single tensor components and combinations, the progression from well to poorly determined parameters follows the trend of ρ, xc, yc, w, b, z, to t. Ranking the estimated model errors from a range of models showed that data sets consisting of concatenated components produced the smallest parameter errors. For data sets comprising combined tensor components, the invariants of the tensor produced the smallest parameter errors. Of the single tensor components, Tzz gave the best performance overall, but those single components with strong directional sensitivity can produce some individual parameters with smaller estimated errors (e.g., w and xc estimated from Txx).
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.044 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".