A pan-Canadian calibration of micro-X-ray fluorescence core scanning data for prediction of sediment elemental concentrations
Bibliographic record
Abstract
Sediment geochemistry is one lens through which lake sediments are studied to reconstruct local and regional environmental processes. The measurement of sediment elemental composition has historically relied on expensive and destructive methods that limit the spatial and temporal scale of study. Micro-X-ray fluorescence (µXRF) core scanning offers a non-destructive, high-resolution alternative, but its results (i.e., intensity expressed as counts per second) are considered semi-quantitative and comparison among sites requires calibration. Calibration methods are emerging, although they are not yet widely employed and require further assessment of their efficacy. Using 135 sediment samples from 48 lakes across Canada, we assessed the congruence between µXRF and conventionally measured element compositions with various normalization and calibration techniques. Normalization of µXRF data to common proxies (e.g., Ca, Si, Ti, coherence:incoherence ratio, and total counts per second) often improved correlations between µXRF and conventional data, but increases were modest and not consistent for all elements. Our results suggest that µXRF normalization techniques should be applied cautiously, as no proxy represents a “one-size-fits-all” solution. The performance of multivariate log-ratio calibration (MLC) was more consistent, yielding moderate to strong improvement of the correlations between reference and predicted element concentrations. Random forest regression models outperformed partial least squares regression models for almost all elements. MLC may be applied where knowledge of elemental concentration is of great importance, or when comparing across multiple sites with diverse sediment geochemistry. Overall, our results reinforce uncalibrated µXRF core scanning as a strong investigative tool for measuring sediment geochemistry. Although calibrated µXRF data shows promise, conventional methods for measuring sediment geochemistry are still necessary for comparing element concentrations with sediment quality guidelines.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".