A novel approach for tracing mycophagous small mammals and documenting their fungal diets
Bibliographic record
Abstract
We developed a method combining passive baiting (animals that are not trapped) with DNA meta-barcoding of the feces acquired, to study fungi in the diet of small mammals. Mammal and fungal species were identified using genomic DNA of 596 fecal samples collected in five regions of the eastern Canadian boreal forest. For identification of the small mammal species, the cytochrome b region was used. A total of eight species of small mammals displayed hypogeous fungi consumption, with northern flying squirrels (Glaucomys sabrinus) and red-backed voles (Myodes gapperi) as the top consumers. For identification of their fungal diets, the ribosomal internal transcribed spacer (ITS) region was used. We recovered 722 taxa of Ascomycota, 429 Basidiomycota, 81 Zygomycota, 4 Chytridiomycota, 1 Glomeromycota, and 44 unidentified fungal taxa. Of these, 28 were hypogeous sequestrate fungi (underground fructification), which presumably are dug out by small mammals for consumption. Otherwise, for the remaining fungi [epigeous (above ground fructification) or microscopic fungal species], it is unclear which ones are selected by the animal as a dietary source or result from incidental contamination. Our paper presents a promising approach for tracing mycophagy in small mammals, and our results suggest that fungal diversity is important for the diet of some small mammals.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".