Dispersal of an herbaceous perennial,<i>Paeonia brownii</i>, by scatter-hoarding rodents
Bibliographic record
Abstract
Most plants that are dispersed by seed-caching animals are large, woody trees that produce large, nutritious nuts. But a few species dispersed in this way are relatively small shrubs or perennial herbs. Wild peony (Paeonia brownii) is a perennial herb in western North America that is dispersed by seed-caching rodents such as chipmunks (Tamias sp.), deer mice (Peromyscus maniculatus), and pocket mice (Perognathus parvus). These rodents harvest seeds from the dehiscent, pendant pods and transport them short distances (most <20 m) and cache 1 or a few seeds from 0 to 15 mm deep in soil. Unrecovered seeds germinate in the spring. Unlike most nuts, peony seeds are not highly preferred food items; they are rich in carbohydrates and low in lipids and protein. Rodents remove peony seeds slowly compared to Jeffrey pine (Pinus jeffreyi) seeds, which are highly preferred by rodents and dispersed in the same manner. The low preference for peony seeds may benefit the plants: peony seeds are slow to be harvested and cached, but also slow to be removed from caches and eaten. Small herbaceous plants cannot produce large crops of large, attractive seeds to satiate potential seed dispersers, as do most nut-bearing trees, so producing low-preference food items probably helps these types of plants to ensure that some of the seeds survive to germinate.
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 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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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; both teacher heads agree on what is shown here.
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".