Plant—animal interactions and climate: Why do yellow pine chipmunks (<i>Tamias amoenus</i>) and eastern chipmunks (<i>Tamias striatus</i>) have such different effects on plants?
Bibliographic record
Abstract
Climate can shape the nature of plant-animal interactions. The yellow pine chipmunk (Tamias amoenus), which occupies semi-arid pine forests of western North America, engages in mutualistic relationships with its food plants by dispersing seeds. These chipmunks scatter hoard seeds during spring, summer, and early autumn in soil; unrecovered seeds germinate in the spring. The eastern chipmunk (Tamias striatus) of eastern North America occupies mesic deciduous forests. These chipmunks primarily larder hoard seeds and nuts. Seeds in burrow larders cannot establish seedlings, so these chipmunks have not been documented to be involved in mutualistic plant-animal interactions. The differences in behaviour and the roles that these 2 species play in their communities appear to be caused by differences in precipitation. Scattered caches are more secure from pilferers in the dry western environment because olfaction is moisture dependent. In the more mesic eastern forests, scattered caches are quickly pilfered, causing chipmunks to store most food in larders, which they defend. Climate (i.e., amount of precipitation) also influences many other aspects of the ecology of these species, including home range size, the size and role of the home burrow, mode of foraging, and diet.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".