Gravity Gradiometry – Today and Tomorrow
Bibliographic record
Abstract
Gravity gradiometry is coming of age as a standard exploration process. The acceptance and scope of airborne surveys is on the rise, with success stories published and documented. A renewed interest in marine surveys for hydrocarbons is also occurring. New sensor and system developments are nearing a point where they may be ready for field tests and commercial deployment. It is accurate to say that the state of gravity gradiometry is healthy in today’s commercial marketplace. As good as this is, there remain a number of challenges and opportunities for full utilization of gravity gradiometry as a tool for the explorationist. A number of questions and concerns need to be addressed ranging from sensor performance to operational efficiency to data handling to educating users. While these challenges might seem daunting, the future looks bright for gradiometry as innovation and acceptance continue to grow. In fact, the time seems right to ask some thought-provoking “What if” questions: Is the era of gradiometry just beginning to dawn? How will the future unfold for this capability? What is the optimal exploration system? What if multiple gravity components could be measured at the same time (i.e. scalar gravity, 2nd order tensors, and 3rd order tensors)? Are the physical limits of measurement already being met? What if data were available without limits throughout the world?
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.008 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".