Application of molar element ratio analysis of lag talus composite samples to the exploration for iron oxide–copper–gold mineralization: Mantoverde area, northern Chile
Bibliographic record
Abstract
A molar element ratio analysis based on whole-rock, multi-element geochemical data for regional lag talus composite samples is evaluated as an exploratory method for iron oxide–copper–gold mineralization (IOCG) in hyperarid settings. This study is focused in the now hyperarid Mantoverde area, III Región of northern Chile (latitude 26°01′ to 26°53′S), and comprises the Andean Coastal Cordillera and Central Valley. The area contains numerous structurally controlled iron oxide-Cu-(Au) prospects and deposits hosted by calc-alkaline volcanic and plutonic rocks of Jurassic–Cretaceous age. Comparison with data for outcrop samples indicates that lag talus composite samples reflect the composition of bedrock in terms of major and selected trace elements. As with outcrop samples, zirconium is the most conserved element in the talus samples, and is therefore used as common denominator in different molar ratios. A molar element ratio analysis using geochemical data from talus composite samples indicates that rocks associated with mineralization have been K-enriched and Na-depleted. Consequently, gradients in the K/Al and Na/Al molar ratios are useful targeting parameters. Similarly, a lithogeochemical alteration index, recently defined by the authors, quantifies the degree of hydrothermal alteration of host rocks and can be used to both target potential anomalous sectors (strongly altered rocks) and delimit barren areas. It is evident that lag talus composite samples constitute a reliable and effective sampling medium in regional exploration programmes for IOCG deposits in hyperarid settings.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".