Calibrated Uncertainty Estimation for Soil Organic Carbon from Raman Spectra
Bibliographic record
Abstract
Machine learning (ML) is a powerful tool for inferring chemometric properties from Raman spectra, expanding the information extractable from high-dimensional spectral data. A growing application is the estimation of soil organic carbon (SOC), where ML models relate overlapping Raman and fluorescence features to chemical composition. However, these models typically lack calibrated, prediction-level uncertainty estimates that limit their utility in decision-critical contexts. We present a framework for quantifying predictive uncertainty in SOC estimation from Raman spectra using Shifted Excitation Raman Difference Spectroscopy (SERDS). The approach employs conformal prediction (CP) to generate statistically valid prediction intervals using a held-out calibration data set and is compatible with a variety of uncertainty quantification (UQ) methods. To our knowledge, this is the first unified framework that integrates conformal calibration with multiple UQ strategies for Raman-based SOC estimation, addressing both aleatoric (irreducible) and epistemic (reducible) sources of uncertainty in a field-relevant setting. We assess the framework across several regression models, including Deep Ensembles, Bayesian neural networks, Monte Carlo Dropout, quantile regression, and heteroscedastic Gaussian models. All methods, when conformalized, produced well-calibrated uncertainty estimates with narrow prediction intervals, achieving reliable empirical coverage across confidence levels. Ablation studies revealed that many UQ techniques were poorly calibrated without conformalization. Our findings indicate that uncertainty in this task is predominantly aleatoric in nature, suggesting that improvements in predictive performance will depend more on improving spectral quality and preprocessing than on model complexity. This framework provides a practical, generalizable solution for generating trustworthy, calibrated, sample-specific uncertainty estimates in Raman-based chemometric analyses.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".