Anisotropic singularity and application for mineral potential mapping in GIS environments
Bibliographic record
Abstract
The singularity of a geophysical or geochemical data reflects their spatial self-similar or self-affine property.It is useful in data estimation(interpolation),mineral exploration,and environment assessment.The current singularity estimation algorithm assumes the data is isotropic in the vicinity of every location.However,geophysical or geochemical data is often anisotropic,and it is often happened that the most hopeful target in mineral exploration locates at the sub-tectonic structures,where anisotropy occurs,inside the major tectonics.The new anisotropy singularity estimation method is implemented in a GIS environment in this study to use the strong spatial analysis power of GIS.The Bouguer anomaly of the southern Nova Scotia,Canada,is used to demonstrate the anisotropy parameters estimation for mineral potential mapping.The results demonstrated that the given anisotropy singularity method is a power tool in mineral potential assessment.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".