Who is eating what: diet assessment using next generation sequencing
Bibliographic record
Abstract
The analysis of food webs and their dynamics facilitates understanding of the mechanistic processes behind community ecology and ecosystem functions. Having accurate techniques for determining dietary ranges and components is critical for this endeavour. While visual analyses and early molecular approaches are highly labour intensive and often lack resolution, recent DNA-based approaches potentially provide more accurate methods for dietary studies. A suite of approaches have been used based on the identification of consumed species by characterization of DNA present in gut or faecal samples. In one approach, a standardized DNA region (DNA barcode) is PCR amplified, amplicons are sequenced and then compared to a reference database for identification. Initially, this involved sequencing clones from PCR products, and studies were limited in scale because of the costs and effort required. The recent development of next generation sequencing (NGS) has made this approach much more powerful, by allowing the direct characterization of dozens of samples with several thousand sequences per PCR product, and has the potential to reveal many consumed species simultaneously (DNA metabarcoding). Continual improvement of NGS technologies, on-going decreases in costs and current massive expansion of reference databases make this approach promising. Here we review the power and pitfalls of NGS diet methods. We present the critical factors to take into account when choosing or designing a suitable barcode. Then, we consider both technical and analytical aspects of NGS diet studies. Finally, we discuss the validation of data accuracy including the viability of producing quantitative data.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".