Mechanisms of truffle detection by northern flying squirrels
Bibliographic record
Abstract
The objective of this study was to evaluate how northern flying squirrels (Glaucomys sabrinus) locate truffles (Gautieria monticola), a subterranean and ephemeral but primary food source. Thus, we evaluated the importance of three factors to the foraging behavior of northern flying squirrels: (i) olfactory chemicals that emanate from truffles; (ii) the presence of coarse woody debris (decaying logs), which are often associated with fungi; and (iii) we explored the potential role animal memory could play in truffle detection as well. In a foraging arena, squirrels successfully retrieved buried truffles that lacked aboveground cues in 19 of 30 trials and failed to search near treatments that lacked truffles altogether, confirming the importance of olfaction to squirrel foraging. However, squirrels also retrieved truffles that were associated most frequently with surface logs (27 of 30). In addition, the initial detection rate of the truffle + log treatment was significantly greater than the truffle-only treatment. Thus, although squirrels search for truffles primarily using olfaction, they may also benefit by searching near coarse woody debris on the forest floor as an aboveground cue to truffle locations. In addition, because 82% of Sierra Nevada truffle-fruiting locations that were marked in 1996 yielded truffles again the following 2 years, mycophagous animals like northern flying squirrels may benefit by memorizing fruiting locations and foraging at these same locations from year to year.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.001 | 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".