Unsupervised classification applications in enhancing lithological mapping and geological understanding: a case study from Northern Ireland
Bibliographic record
Abstract
Pattern classification algorithms can help us to recognize and predict patterns in large and complex multivariate datasets. Self-organizing maps (SOMs), which use competitive learning, are unsupervised classification tools that are considered to be very useful in pattern classification and recognition. This technique is based on the principles of vector quantification of similarities and clustering in a high-dimensional space and the method can handle the analysis and visualization of high-dimensional data. This tool is ideal for analysing a complex combination of categorical and continuous spatial variables, with particular applications to geological features. We used SOMs to predict geological features based on airborne geophysical data acquired through the Tellus Project in Northern Ireland. The SOMs were applied through 20 experiments (iterations), incorporating radiometric data in combination with geological features, including elevation, slope angle, terrain ruggedness and geochronology. The SOMs were able to differentiate contrasting bedrock geology, such as acidic v. mafic igneous rocks, although data clustering over intermediate rocks was less clear. The presence of a thick cover of glacial deposits in most of the study area presented a challenge for data clustering, particularly over the intermediate igneous and sedimentary bedrock types. Supplementary material: Twenty iterations were carried out during this research. Only six are included in the article. The rest of the iterations are available at https://doi.org/10.6084/m9.figshare.c.6603098
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".