What happens on the Yukon River leaves genetic traces; analysis of eDNA samples from a thousand-mile canoe expedition
Bibliographic record
Abstract
In the summer of 2022, I collected eDNA samples on a six-week self-supported expedition along the upper one thousand miles of the Yukon River. While traveling along the upper half of the river, I was able to take samples in many different ecosystems and from different classifications of tributaries that contribute to the main flow of the Yukon. The Yukon and some of the tributaries are known for having high sediment loads. My first five samples were focused on the headwaters of the main Yukon, and sampling upstream and downstream of the two dams supporting the community of Whitehorse. After this, I sampled at the confluences of major tributaries. The samples were then transported back to Fairbanks following the expedition. The fish DNA was extracted from the eDNA filters, and I have been doing the genetics since. Eventually, the samples will be processed utilizing metabarcoding techniques to determine which fish species were present at the various sample sites.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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 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".