Mapping the global origins of soybean: a study using ICP-MS and chemometrics
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
To enhance transparency in the soybean supply chain and help prevent misrepresentation of geographic origin, an analytical method combining ICP-MS with chemometrics was developed. A total of 422 soybean samples were collected from Brazil, the United States, Argentina, China, India, Paraguay and Canada, representing over 95% of global production. The OPLS-DA multivariate analysis model used for classification achieved 98.5% accuracy, with Ni, Na, Mo, Ba, Co, Cr, Cd, Sr, Se, K and Ca identified as key elements for origin differentiation. This approach provides a practical tool for companies and regulators to verify geographic origin, supporting compliance with trade and sustainability requirements and tariff-related controls. Additionally, the ability to differentiate soybean samples from various regions within Brazil and the United States was investigated and preliminary comparisons of meal samples from deforested and non-deforested areas in Brazil revealed elemental differences, suggesting potential environmental influences and highlighting the need for further investigation.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 it