Experimental design considerations for assessing marine biodiversity using environmental DNA
Bibliographic record
Abstract
The Centre for Environmental Genomics Applications (CEGA) is a new research facility in Eastern Canada dedicated to the development of environmental DNA (eDNA) approaches to biomonitoring and biodiversity assessment with a focus on marine environments. Genomics data has become a major source of biological information and, unlike conventional monitoring, offers the potential for near real-time biological tracking of any ecosystem. In transitioning from proof of concept studies to real-world applications, the reliability and robustness of eDNA sampling approaches must be examined. Here we present one pilot project measuring the effect of sample volume and number of replicates on eDNA-based biodiversity surveys of aquatic eukaryotes. Specifically, we examine if many small volume samples capture greater marine biodiversity than fewer large volume samples and whether this pattern is consistent at site-level and transect-level. Three to five replicate surface water samples of two different volumes were collected from eight sites along two transects in Conception Bay, Newfoundland. Multiple DNA marker regions were sequenced from these samples and statistically analyzed using both taxonomy dependent and taxonomy independent approaches. Methodological validation is an essential step towards standardization and implementation of genomic tools in routine environmental monitoring.
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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 | 0.065 | 0.088 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".