Applications of machine vision and machine learning for deposit characterization at the Castelo de Sonhos paleoplacer gold deposit, Brazil
Bibliographic record
Abstract
Paleoplacer gold deposits represent a significant source of global gold production. The Castelo de Sonhos deposit in Brazil is a promising candidate for development and contribution to the paleoplacer proportion of gold production. Mineralization in the deposit is associated with metamorphosed conglomeratic units. This study applies machine vision methods to RGB drill-core imagery to identify and quantify the characteristics of large clasts (pebble to cobble-sized) within these units. Using this workflow, we demonstrate that gold-bearing material is preferentially found in specific ranges of clast geometry criteria, corresponding to an optimal depositional environment, offering a scalable and internally consistent support for manual logging. This approach enables rapid assessment of mineralization potential to support drilling decisions, and in the production environment, would enable rapid and robust material sorting, lowering costs associated with siliceous cobble-rich ore. These findings highlight the value of machine vision methods integrated with geochemical data and interpretation to enhance both exploration and operational efficiencies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".