3D inversion of airborne gravity gradiometry data for the Budgell Harbour Stock, Newfoundland: A case history using a probabilistic approach
Bibliographic record
Abstract
ABSTRACT We have studied the Mesozoic Budgell Harbour Stock, a gabbroic intrusion in north-central Newfoundland, Canada, using 3D inversion of airborne gravity gradiometry data based on a probabilistic inversion method. Significantly, differences were observed between the results when inverting the single Tzz component and when inverting the 5C combination. We also found that the Tzz model failed to reproduce the long-wavelength signals from other components, whereas the model recovered from five components accommodated all of the signals from all of the components. To estimate the influence of long-wavelength signals from targets other than the intrusion, such as deeper bodies or large-scale terrain variations, inversion tests are performed on a synthetic model. The inversion results for the synthetic example indicate that the joint inversion of five components is more sensitive to long-wavelength signals, which can generate spurious structures to fit all of the signals from the five components. In contrast, the Tzz model is less affected by the long-wavelength signals and thus tends to produce a stable solution, despite failing to incorporate all of the long-wavelength signals from the tensor data. We found that gravity gradiometry data could be used to delineate the intrusion within this study area, which is also consistent with the susceptibility model recovered from inversion of aeromagnetic data and with results from a previous geophysical study. Moreover, the differences between the Tzz model and the 5C model may reflect the long-wavelength signals in the gravity gradiometry data.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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".