Density/area power-law models for separating multi-scale anomalies of ore and toxic elements in stream sediments in Gejiu mineral district, Yunnan Province, China
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract. This contribution introduces a fractal filtering technique newly developed on the basis of a spectral energy density vs. area power-law model in the context of multifractal theory. It can be used to map anisotropic singularities of geochemical landscapes created from geochemical concentration values in various surface media such as soils, stream sediments, tills and water. A geochemical landscape can be converted into a Fourier domain in which the spectral energy density is plotted against the area (in wave number units), and the relationship between the spectrum energy density (S) and the area (A) enclosed by the above-threshold spectrum energy density can be fitted by power-law models. Mixed geochemical landscape patterns can be fitted with different S-A power-law models in the frequency domain. Fractal filters can be defined according to these different S-A models and used to decompose the geochemical patterns into components with different self-similarities. The fractal filtering method was applied to a geochemical dataset from 7,349 stream sediment samples collected from Gejiu mineral district, which is famous for its word-class tin and copper production. Anomalies in three different scales were decomposed from total values of the trace elements As, Sn, Cu, Zn, Pb, and Cd. These anomalies generally correspond to various geological features and geological processes such as sedimentary rocks, intrusions, fault intersections and mineralization.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it