Multivariate analysis for geochemical process identification using stream sediment geochemical data: A perspective from compositional data
Bibliographic record
Abstract
Identification of underlying geochemical processes based on samples of different types such as stream sediments, soils, and water is important for a range of applications including mineral exploration, land use planning, and environmental assessment of both natural and anthropogenic factors. However, almost all geochemical compositions of these samples are subject two limitations: outliers and the data closure effect. In the present study, bivariate relationships between selected major elements are examined to illustrate their spurious correlation by using centered log ratio (clr) transformation. In addition, robust factor analysis (FA) and compositional data analysis are used to prevent the effect of outliers and to reduce the influence of data closure in the identification of geochemical processes. First, a k-means algorithm is applied to partition geochemical data into three clusters to enhance the interpretation of the geochemical data. Then, robust FA is applied to log ratio-transformed geochemical data. The first five factors are extracted on the basis of the scree plot of eigenvalues. The results indicate that robust FA applied to log ratio-transformed data can be used to effectively identify geochemical processes and to determine the extent of anthropogenic and natural influences such as mineralization, weathering and diagenesis, heavy metal accumulation or contamination, or a combination of these factors. Several geochemical processes are indicated by the first five factors, explained as follows: (a) F1 reflects granitic rocks and natural or industrial contamination by Cu, Ni, Sb, As, Cd, and Cr; (b) F2 reflects W polymetallic mineralization; (c) F3 reflects Au anomalies and heavy metal contamination by Zn, Cd, Mn, and Pb; (d) F4 reflects Mo and Au anomalies; and (e) F5 reflects Ag-W-Be-La mineralization and heavy metal contamination by Hg and Sb.
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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 | 0.008 | 0.015 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".