Comparison of AIRGrav and GT-1A airborne gravimeters for research applications
Bibliographic record
Abstract
Abstract Airborne gravimetry has played a vital role in contributing to our knowledge of the subglacial environment in polar regions. Previous programs have produced extensive gravity data sets in Antarctica, but the resolution and accuracy of the data have been limited. We have evaluated the relative performance and suitability of two different airborne gravimeters for research applications from flight tests over the Canadian Rocky Mountains near Calgary. Survey design, mission profiles, and demands on the performance of an airborne gravimeter are different for the remote polar environment than for most commercial exploration surveys. Both systems, the AIRGrav and GT-1A, can produce higher-resolution data with improved flight efficiency than can the BGM-3 and LaCoste & Romberg gravimeters used in Antarctica. The AIRGrav and GT-1A systems are capable of draped flying of airborne gravity, allowing new applications for polar use. Both systems could provide the academic community with a significant increase in accuracy and horizontal resolution to enable major advances in understanding the subglacial environment. Compared to the GT-1A system, the AIRGrav system has a lower noise level and higher accuracy, and it is less sensitive to changing flight conditions — in particular, vertical accelerations during turbulent flights.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".