Multi-element association analysis of stream sediment geochemistry data for predicting gold deposits in south-central Yunnan Province, China
Bibliographic record
Abstract
The use of traditional statistical and new methods of spatial analysis can provide suitable indicators of geochemical element dispersion, and aid in targeting potential areas for mineral exploration. A case study involving the analysis of stream sediment geochemistry data from an area in Yunnan province, South China, is presented. The area has two known areas of gold deposits where several mines are present. The study starts with an appraisal of the descriptive statistics of the data. Geological studies were conducted in the study area to obtain a thorough understanding of the regional geology, ore geology of known gold deposits, and mineralization, and to determine the mineral deposit model. Most of the GIS analysis was done using the stream sediment geochemical data. An inverse distance weighting interpolation algorithm was used to convert the point data to continuous surface (grid) maps for each element. Principal component analysis (PCA) was used to compress the information to a few maps and to assist in determining multi-element associations. The study revealed that most of the high element concentrations in stream sediments were found in the Ailaoshan metamorphic belt. Gold deposits were found to be associated with ultramafic intrusives within the Ailaoshan metamorphic belt and the ultramafic instrusives are associated with principal component images that represent multi-element associations related to gold mineralization. The first two principal components possibly represent two different types of gold associations and phases of mineralization. The study demonstrates the usefulness of applying PCA to geochemical data to produce maps that reveal different associations useful for gold exploration.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".