Compositional balance analysis for geochemical pattern recognition and anomaly mapping in the western Junggar region, China
Bibliographic record
Abstract
The western Junggar region of China is endowed with considerable resources of gold, copper and chromite. Complex geological conditions and diversity deposit types could lead to various geochemical patterns and geochemical anomalies. In this study, compositional balance analysis (CoBA) is demonstrated for recognition of geochemical patterns and mapping geochemical anomalies that are closely associated with gold/copper/chromite mineralization and particular geological units in the western Junggar region. Here, CoBA was based on hierarchical cluster analysis and sequential binary partition technique, which provides a new path for intuitively distinguishing particular relationships between groups of parts that are of interest. To recognize anomalous patterns in stream sediment geochemical data that are closely associated with mineralization, 18 geochemical elements were used to construct 17 balances by means of the CoBA method, of which four key balances were selected for further investigation. Relevant geological information (e.g. mineral deposit occurrences) provides important references for interpreting and validating the results. By comparison of geochemical patterns of the square roots of Au and Cu concentrations with that of factor scores, the results indicate that the CoBA method provides straightforward and robust interpretation of stream sediment geochemical data by suppressing background patterns and enhancing anomalous patterns in the western Junggar, China.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".