Regional airborne gravity surveys in Western Australia: Considerations for the end user
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
SummaryRegional airborne gravity surveys are being acquired over much of the State of Western Australia by the Geological Survey of Western Australia (GSWA) and Geoscience Australia (GA) to provide coverage where existing ground gravity coverage is sparse. The acquisition and processing of these surveys poses several challenges.The data acquired by Sander Geophysics (SGL) using the AIRGrav system in Western Australia during 2018 was done so without control lines for reasons of cost efficiency, relying on the ground gravity to provide the necessary levelling corrections. Methodologies have been developed to achieve effective levelling under these circumstances, although the final result varies depending on the methodology used. Data acquired on earlier surveys with control lines are being used to compare and contrast to data acquired without them. Ongoing power spectrum analysis suggests a way in which the different methods may be judged objectively.Horizontal components of gravity are also acquired by AIRGrav. Levelling these components is a challenge under all circumstances. The relationships between the components expressed in potential field theory allow the different components data to be compared and checked for consistency.Digital elevation model (DEM) data acquired during the surveys provide a means for checking other sources of DEM typically employed for applying terrain corrections. The impact of inaccurate DEM data on the corrected gravity data overall is small but can be locally significant. Data quality of the regional surveys is high, but the end user should be aware of the limitations posed by the choices made in data acquisition and processing.
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.
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 | 0.000 |
| 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) | 0.001 | 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 it