Rotation-based outlier detection for geochemical anomaly identification in stream sediment multivariate data
Bibliographic record
Abstract
Abstract This research explores the use of the rotation-based outlier detection (ROD) method for identifying geochemical anomalies in a multivariate stream sediment dataset from Iran, targeting porphyry and vein-type Cu mineralization. Geochemical datasets often present challenges for outlier detection methods like local outlier factor (LOF) and k-nearest neighbor (KNN), which rely on distance or density metrics and require parameter tuning (e.g., neighborhood size k). High-dimensional feature spaces further complicate their application. ROD, in contrast, offers a parameter-free, rotation-based approach that effectively analyzes geometric relationships between samples in subspaces, mitigating the curse of dimensionality. This makes ROD particularly suited to high-dimensional geochemical datasets, where complex relationships between elements (due to lithology or mineralization) are critical for identifying anomalies. This study compares ROD with LOF and KNN using two subsets of geochemical variables (Ag, As, Au, Bi, Co, Cr, Cu, Mo, Ni, Pb, Sb, Zn; and Ag, As, Au, Cu, Mo, Sb) and evaluates its performance based on the receiver operating characteristic (ROC) analysis and the number of known mineral occurrences detected in anomaly class. ROD outperforms LOF and KNN, capturing 78% (14 out of 18) of known Cu-bearing mineral occurrences. Moreover, ROD shows better conformity between 10% of highest outlier scores and Cu-mineralization sites. Rotation cost function in ROD, evaluated using the median absolute deviation (MAD), enhances its ability to detect outliers by focusing on orientation rather than distance, and by reducing noise misclassification. In addition, the parameter-free design of ROD and improved handling of high-dimensional data makes it a promising tool for geochemical exploration, as it captures unique mineralization-related signals that might be missed by traditional methods.
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.
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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".