3D joint inversion of airborne gravity gradiometry and magnetic data: A case study from Budgell Harbour Stock in northern-central Newfoundland, Canada
Bibliographic record
Abstract
We have studied an alkaline ultramafic intrusion in northern-central Newfoundland, Canada, using 3D joint inversion of airborne gravity gradiometry and magnetic data. The intrusion is of interest as it represents a candidate for platinum group metal mineralization of potentially economic concentrations. We developed a joint inversion algorithm based on a probabilistic method and applied it to airborne gravity gradiometry and magnetic data-sets to obtain density and magnetic susceptibility distributions. The joint coupling measure is incorporated in the inversion by using a cross covariance matrix of density and magnetic susceptibility, by which structural orientation and spatial extent of the intrusion can be efficiently integrated. A correlation coefficient defined by users can control the level of similarity between the two models. Compared with the separate inversions, better definition and location of the main intrusion as well as associated lamprophyre dykes at shallow depth was achieved by the joint inversion. Presentation Date: Tuesday, September 17, 2019 Session Start Time: 8:30 AM Presentation Time: 11:25 AM Location: 214D Presentation Type: Oral
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".