Improved well log classification using semi-supervised algorithms
Bibliographic record
Abstract
Many geophysical problems have an abundance of unlabeled data and a paucity of labeled data, and lithology classification of wire-line data reflects this situation. Training supervised algorithms on small labeled data sets can cause over-fitting, and subsequent predictions for the numerous unlabeled data may be unstable. However, semi-supervised algorithms are designed for classification problems with limited amounts of labeled data and are theoretically able to achieve better accuracies than supervised algorithms in these situations. We explore this hypothesis by applying two semi-supervised techniques to a well log dataset and compare their performance to three supervised algorithms. Our findings suggest that the semi-supervised methods we considered can match or outperform supervised methods if the model assumptions are met. Presentation Date: Wednesday, September 18, 2019 Session Start Time: 1:50 PM Presentation Time: 3:55 PM Location: 221D Presentation Type: Oral
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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".