Development and application of feature engineered geological layers for ranking magmatic, volcanogenic, and orogenic system components in Archean greenstone belts
Bibliographic record
Abstract
Geologically representative feature engineering is a crucial component in geoscientific applications of machine learning. Many commonly applied feature engineering techniques used to produce input variables for machine learning apply geological knowledge to generic data science techniques, which can lead to ambiguity, geological oversimplification, and/or compounding subjective bias. Workflows that utilize minimally processed input variables attempt to overcome these issues, but often lead to convoluted and uninterpretable results. To address these challenges, new and enhanced feature engineering methods were developed by combining geological knowledge, understanding of data limitations, and a variety of data science techniques. These include non-Euclidean fluid pre-deformation path distance, rheological and chemical contrast, geologically constrained interpolation of characteristic host rock geochemistry, interpolation of mobile element gain/loss, assemblages, magnetic intensity, structural complexity, host rock physical properties. These methods were applied to compiled open-source and new field observations from Archean greenstone terranes in the Abitibi and western Wabigoon sub-provinces of the Superior Province near Timmins and Dryden, Ontario, respectively. Resulting feature maps represent conceptually significant components in magmatic, volcanogenic, and orogenic mineral systems. A comparison of ranked feature importance from random forests to conceptual mineral system models show that the feature maps adequately represent system components, with a few exceptions attributed to biased training data or limited constraint data. The study also highlights the shared importance of several highly ranked features for the three mineral systems, indicating that spatially related mineral systems exploit the same features when available. Comparing feature importance when classifying orogenic Au mineralization in Timmins and Dryden provides insights into the possible cause of contrasting endowment being related to fluid source. The study demonstrates that integrative studies leveraging multi-disciplinary data and methodology have the potential to advance geological understanding, maximize data utility, and generate robust exploration targets.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".