Application of Autoencoders Neural Network and K-Means Clustering for the Definition of Geostatistical Estimation Domains
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Bibliographic record
Abstract
The objective of this study was the definition of estimation domains through the application of an artificial neural network Autoencoders and K-Means clustering.The study was based on the analysis of 5,654 composites obtained from an exploratory drilling campaign in a copper deposit.The specific architecture of the autoencoder included an encoder and a decoder, each composed of multiple layers and ReLU activation functions.The encoder, with four hidden layers of 600, 600, 800 and 10 neurons, respectively, was complemented by a decoder that replicated this structure.Application of the K-Means algorithm, with 30 initializations on these encoded representations, culminated in a silhouette score of 0.261 and an inertia of 17,447.44,revealing the optimal formation of two distinct estimation domains: domain 1, with 4,204 samples and an average copper grade of 0.44%, and domain 2 with 1450 samples and an average grade of 0.41% copper.Compared to the geochemical modeling approach in definition of estimation domains, a significant reduction in the mean error (0.29 vs. 0.05) and in the error variance (0.04 vs. 17.36) was observed.In conclusion, this approach not only complements geostatistical estimation techniques, but also improves accuracy and reliability in geological resource estimation.
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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 it