DNA sequencing of Hoary Marmot (M. caligata) stomach contents through metabarcoding
Bibliographic record
Abstract
Mentor: Dr. Diana Wolf; This poster presents our results from using metabarcoding DNA to examine diets of alpine and coastal Hoary marmots. Hoary marmots (Marmota caligata) are herbivores distributed widely throughout alpine habitats from southern Washington, Idaho, and Montana north to the Yukon River in Central Alaska. In Southeast Alaska, however, they are also found at sea level. As the tree line rises in elevation in response to climate change, alpine habitats are expected to shrink. Most hoary marmots occupy alpine tundra and rocky talus. There is an ecological knowledge gap on the diet of M. caligata, including comparing diet at sea level with alpine forage. Determining diet is key to understanding hoary marmots’ ability to thrive on a changing landscape. Alpine-dwelling marmots are thought to feed on grasses, flowering plants, mosses, roots, and lichen. As of yet, we know nothing about the diet of beach-dwelling marmots. We used DNA sequencing (metabarcoding) of M. caligata stomach contents to identify and compare their diets in alpine and sea-level habitats. Our results will help to fill in critical knowledge gaps in hoary marmot ecology and address hoary marmots’ potential resilience to changing climate.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".