Mineralisation predictive targeting using TensorFlow (Google) deep neural networks
Bibliographic record
Abstract
SummarySimple two-layer feedforward supervised neural network (NN) has been described and used for mineral predictive targeting. However, the simple NN has some limitations. For instance, it requires the geophysical responses of over the target be positively high relative to non-target areas.The release of Google’s TensorFlow (TF) for Python (https://www.tensorflow.org/) in 2015 has made it possible to apply the more powerful and robust Deep Neural Network (DNN) to geoscience data for mineral predictive targeting.We test the TF DNN using the magnetic data over a kimberlite in the Canadian Shield and compare the results with those from the simple two-layer NN. The DNN results are better.DNNs are applied to the helicopter TDEM data from Nuqrah, western Arabian Shield and the TDEM from Kabinakagami Lake greenstone belt in Superior craton in Ontario to illustrate the utility of predictive targeting of DNN..
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".