A simple protocol for sampling environmental DNA from flowing waters at remote field sites v1
Bibliographic record
Abstract
We developed this protocol in 2021 to for standardized eDNA sample collection at salmon escapement assessment sites in non-glacial, boreal rivers in the Arctic-Yukon-Kuskokwim Region of Alaska. The protocol is designed to allow researchers without any prior training in molecular techniques to collect eDNA samples and store them for up to several months at remote field camps without access to freezers or refrigeration. The protocol is also designed to minimize contamination at sites where eDNA sampling is conducted by personnel who may have also handled the target species that same day. We designed and implemented this protocol as part of a research project that tested eDNA quantification as a potential approach to estimating the abundance of salmon spawners in rivers. However, the protocol is flexible and could be applied in other projects involving eDNA sample collection from flowing waters at remote sites. We filtered duplicate samples of river water once daily and collected a weekly field blank to assess potential contamination in samples. The levels of replication and sampling frequency can be adjusted to fit the research question as well as budgetary and logistical constraints of the project
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.074 | 0.073 |
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".