What happens on the Yukon River leaves genetic traces; analysis of eDNA samples from a thousand-mile canoe expedition
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 it