The utility of a metagenomics approach for marine biomonitoring
Bibliographic record
Abstract
1 Abstract The isolation and analysis of environmental DNA (eDNA) for ecosystem assessment and monitoring has become increasingly popular. A majority of studies have taken a metabarcoding approach—that is, amplifying and sequencing one or more gene targets of interest. Shotgun sequencing of eDNA—also called metagenomics—while popular in microbial community analysis has not seen much adoption for the analysis of other groups of organisms. Especially in light of the existence of extremely high-capacity DNA sequencers, we decided to test the performance of a shotgun approach side-by-side with a metabarcoding approach on marine water samples obtained from offshore Newfoundland. We found that metabarcoding remains the most efficient technique, but that metagenomics also has significant power to reveal biodiversity patterns, and in fact can be treated as an independent confirmation of ecological gradients. Moreover, we show that metagenomics can also be used to infer factors related to ecosystem health and function.
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How this classification was reachedexpand
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 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.004 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".