Spatial fingerprinting: horizontal fusion of multi-dimensional bio-tracers as solution to global food provenance problems
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
Building the capacity of efficiently determining the provenance of food products represents a crucial step towards the sustainability of the global food system. Whether it is for enforcing existing legislation or providing reliable information to consumers, technologies to verify geographical origin of food are being actively developed. Biological tracers (bio-tracers) such as DNA and stable isotopes have recently demonstrated their potential for determining provenance. Here we show that the data fusion of bio-tracers is a very powerful technique for geographical provenance discrimination. Based on 90 individuals of Sockeye salmon that originate from 3 different areas for which we measured 17 bio-tracers, we demonstrate that increasing the combined bio-tracers results in stronger the discriminatory power. The generality of our results are mathematically demonstrated under simplifying assumptions and numerically confirmed in our case study using three commonly used supervised learning techniques.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| 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 it