Random forest rock type classification with integration of geochemical and photographic data
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Systematic manual and algorithmic classification workflows to characterize rock types are increasingly applied in the mineral exploration and mining industry, leveraging large systematically collected datasets. The aim of these are robust and repeatable classifications to aid more traditional visual logging practices. This study uses random forest algorithms to examine the impacts of integrating distinct datasets with complementary characteristics; chemistry to enable compositional distinctions, and photography to enable textural distinctions. We use a random forest classifier to examine the accuracy metrics of models producing rock type classifications using these two data types independently and integrated together. Prediction accuracy, measured using 10-fold cross validation, was 87% for geochemical-only inputs, 85% for photographic-only inputs, and 90% for mixed inputs from both datasets. A mining and exploration project in the Late Miocene to early Pliocene porphyry belt in Chile is the site of this case study, where datasets were systematically acquired using in-field methods on historical drill-cores. Results indicate that classification of lithology is improved by integration of photography-based and composition-based feature inputs. We infer that the benefits of integration would increase in proportion with increasing compositional similarity between rock types. This approach might also be applied to similar geological problems, such as alteration or metallurgical classifications; and with somewhat distinct datatypes, such as geochemical interval data and photographic metric extraction from coincident intervals in core photos.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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