Predictive Geological Mapping of the Baie Verte Peninsula, NL, CA, Using SOM Neural Nets
Bibliographic record
Abstract
Summary Geophysical data has been interpreted one data-type at a time using primarily visual inspection by the interpreter. This approach become increasingly difficult as the dimensionality (e.g. number of data-types) of the dataset is increased. Self-organizing maps (SOMs) are a class of unsupervised artificial neural network algorithm which are used to cluster multi-dimensional data while preserving the overall topology of the original dataset. Geophysical responses are closely linked to the local geology it is postulated that SOMs can be employed to cluster multi-dimensional geophysical data in order to produce predictive geological maps. In the development of an effective work flow for creating predictive geological maps using SOMs the predictive maps are compared to a known geology. This comparison can be done through visual inspection. However, quantitative measures of clustering quality are also desired. SOM predictive mapping was applied to mapping the Baie Verte Peninsula on the north coast of the island of Newfoundland, Canada. The Baie Verte Peninsula is a region of complex geology with good regional geophysical data coverage and good regional geological mapping. The predictive maps produced in this project are evaluated both through visual inspection and quantitatively using a set of clustering quality measures.
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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".