Bibliographic record
Abstract
Two gravity gradiometer designs are currently available for commercial survey operations and other instruments are in the late stages of pre-deployment research. These gradiometer systems differ from one another with respect to the number and orientation of sensing accelerometer pairs. There is a need for a theoretical framework to evaluate how these design variations, affect the expected performance of these devicesThree methods of design comparison will be presented: 1) Transformation of noise, a calculation of the degree of noise reduction produced by the method of transforming and combining measured gradient components. 2) Inversion errors, comparison of the degree to which noise induces errors in the values of parameters determined in a parametric inversion calculation. 3) Sensitivity, analysis of the response of each system to a point source and how that source varies as a function of location in 3D space. Each of these methods focuses on a different aspect of the practice of gravity gradiometry. Specifically, noise, inversion and source detection.Analysis will be centered on comparison between the two gravity gradiometer designs manufactured by Lockheed-Martin. The full tensor gradiometer (FTG) and the horizontal partial tensor gradiometer that is part of the Falcon survey system. All three methods predict that in order for these two gradiometer designs to yield equivalent results the noise level of the horizontal partial tensor gradiometer must be less than that of each of the three sub-gradiometers of the FTG by a factor of 3.08.
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.
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 | 0.007 | 0.022 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".