Pseudo-shot Learning for Soil Classification With Laser-Induced Breakdown Spectroscopy
Bibliographic record
Abstract
Laser-induced breakdown spectroscopy (LIBS) has become an emerging analytical technique for soil analysis. The application of machine learning for quantitative and qualitative analysis has made LIBS more promising. However, the emission line distribution can be highly variable due to the soil samples' varied physical properties and/or chemical composition. It may cause spectra distribution change and make the training spectra not accurately reflect the test spectra distribution. Hence, the test performance is deteriorated by applying an ML model trained on samples from the training distribution to the test distribution. To handle the spectra distribution problem, we propose using pseudoshot learning with Siamese networks, a domain adaptation (DA) method to incorporate pseudolabeled samples based on similarity confidence into the parameter estimation procedure. Considering the domain transfer differences among classes, we categorize the classes as hard, normal, and easy to reflect the class transfer difficulties in DA. We mainly focus on the hard classes as samples from these classes are not representative of the source domain and can easily be misclassified in the prediction phase. Few-shot learning is used to find the spectra from hard classes but misclassified into their similar classes. These spectra are included to cotrain the model with source samples to improve the test performance of hard classes. Our framework is tested with the EMSLIBS dataset, which shows that it can effectively overcome the spectra distribution shift and achieves 94.12% test accuracy. It beats the current best-performing model using the same dataset.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".