Assessment of Environmental <scp>DNA</scp> Survey Design for the Detection of Freshwater Unionid Mussels
Bibliographic record
Abstract
ABSTRACT Implementation of environmental DNA (eDNA) for environmental consultation in association with permitting purposes has been rare within the United States. In part, this is due to the lack of developed standards and guidelines needed to design robust eDNA surveys. This study provides a descriptive analysis for assessing freshwater mussel eDNA detection compared to an exhaustive visual mussel search. We evaluated an eDNA survey at two different levels of sampling effort: (1) at the transect level assessing the collection of eDNA along transects and (2) at the water sample replicate level assessing species detections obtained from subsamples within a transect. Logistic regression assessed eDNA detection probability against the visually observed abundance for each mussel species, informing the level of effort required to detect rare mussel species. This study offers critical insight into survey design guidelines that will be instrumental for building confidence for the implementation of eDNA into freshwater mussel assessments.
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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 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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 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".