Curvature of a geometric surface and curvature of gravity and magnetic anomalies
Bibliographic record
Abstract
ABSTRACT Curvature describes how much a line deviates from being straight or a surface from being flat. When curvature is used to interpret gravity and magnetic anomalies, we try to delineate geometric information of subsurface structures from an observed nongeometric quantity. In this work, I evaluated curvature attributes of the equipotential surface as functions of gravity gradients and analyzed the differences between the theoretical derivation and a practical application. I computed curvature of a synthetic model that consisted of representative structures (ridge, valley, basin, dome, and vertical cylinder) and curvature of the equipotential surface, gravity, and vertical gravity gradient (which is equivalent to the magnetic reduction-to-the-pole result) due to the same model. A comparison of curvature of such a geometric surface and curvature of different gravity quantities was then made to help understand these curvature differences and an indirect link between curvature of gravity data and actual structures. Finally, I applied curvature analysis to a magnetic anomaly grid in the Gaspé belt of Quebec, Canada, to illustrate its useful property of enhancing subtle features.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".