Barcoding the food chain: from Sanger to high-throughput sequencing
Bibliographic record
Abstract
Society faces the complex challenge of supporting biodiversity and ecosystem functioning, while ensuring food security by providing safe traceable food through an ever-more-complex global food chain. The increase in human mobility brings the added threat of pests, parasites, and invaders that further complicate our agro-industrial efforts. DNA barcoding technologies allow researchers to identify both individual species, and, when combined with universal primers and high-throughput sequencing techniques, the diversity within mixed samples (metabarcoding). These tools are already being employed to detect market substitutions, trace pests through the forensic evaluation of trace "environmental DNA", and to track parasitic infections in livestock. The potential of DNA barcoding to contribute to increased security of the food chain is clear, but challenges remain in regulation and the need for validation of experimental analysis. Here, we present an overview of the current uses and challenges of applied DNA barcoding in agriculture, from agro-ecosystems within farmland to the kitchen table.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".