Seed Dispersal by Brown Bears, <em>Ursus arctos</em>, in Southeastern Alaska
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Mammals often consume fleshy fruits and disperse significant quantities of the enclosed seeds. In southeastern Alaska, Brown Bears (Ursus arctos) are among the most important dispersers of seeds for the numerous plant species producing fleshy fruits, because these bears are abundant, often eat large quantities of fruit, and commonly excrete seeds in germinable condition. Scat analyses showed that Brown Bears on Chichagof Island ate increasing quantities of fruit through summer and fall. Scats commonly contained several thousand seeds, often of two or more species. Four kinds of seeds of fleshyfruited plants that normally grow in forest understory germinated at similar levels when experimentally deposited (in bear scats) in the two most common habitats (forest and muskeg), suggesting that habitat distribution of these plants is not determined simply by germination patterns. Although seed passage through bear digestive tracts and the composition of scats are known to affect germination rates to some degree, the most important role of bears in seed dispersal is probably transport.
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.
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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it