Exploratory Data Analysis: Connecting X-Ray Diffraction and Lithogeochemical Data
Bibliographic record
Abstract
The Vazante Group is a district in east-central Brazil that consists of a carbonate-dominated marine platform sequence of Late Mesoproterozoic age. The district is home to a north-south belt that contains several zinc mines, but the petrogenesis of the ore body is not yet fully understood. Samples from this district were analyzed using both X-Ray Diffraction (XRD) and lithogeochemical assay techniques by Dr. Neil Fernandes in 2016. An analysis was conducted in order to explore the statistical correlations between the XRD and lithogeochemical test results. The purpose of the analysis was to determine whether the raw (uninterpreted) XRD data alone could be used to identify the samples enriched in zinc and other elements indicative of economic mineralization. The results showed very subtle trends that were not significant enough to make conclusions about the possibility of using XRD data without accompanying lithogeochemical data. The higher-than-average correlation of the intensity of pyrite peaks in the XRD data with elements associated with mineralization suggests that there are potentially more robust and significant trends that were not fully uncovered by this analysis, as pyrite has already been associated with mineralized zones. The analysis process itself could be valuable in future projects, and future work on this technique is proposed that uses machine learning to cluster the data and detect trends that may not be obvious using conventional techniques.
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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 | 0.011 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.009 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".