The Benefits of Wide Line Spaced Airborne Gravity Gradiometry On Regional Surveys
Bibliographic record
Abstract
SummaryTo stimulate the development of natural resources most governments maintain a basic national geophysical database to outline regional structural geology and basin geometries. Magnetics and gravity are normally the tools of choice. There is an obvious trade-off between cost and detail. Airborne gravity gradiometry can be configured to optimise this trade off.It is known that gravity gradiometry detects shorter wavelengths then what is possible with conventional airborne gravity but longer wavelengths are also captured on regional surveys as is demonstrated with an example survey. An airborne gravity gradiometry survey was conducted in Arnhem Land, Northern territory, that was previously covered by conventional airborne gravity. Analysis of the data shows that the conventional airborne gravity dataset has limited content at spatial wavelengths shorter than 4 kilometre whereas the gravity gradiometry data resolves wavelengths shorter than 1 km, while also maintaining the long wavelength information.The analysis indicates that airborne gravity gradiometry offers better resolution at the same line spacing. An additional benefit is the option to infill areas of interest to capture detail not possible with conventional airborne gravity. This enables more effective use of the regional gravity gradiometry data in an exploration programme.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".