Density-dependent hoarding by rodents contributes to large variation in seed mass of the woodland herb Symplocarpus renifolius
Bibliographic record
Abstract
We analyzed effects of seed-hoarding by rodents on the variation in seed mass and seed success for a perennial forest undergrowth plant — Symplocarpus renifolius Schott ex Miquel — in Hokkaido, northern Japan. Although density of rodents differed greatly between seasons, more rodents were always captured in mesic Sasa sp. patches with dense foliage than in wet Lysichiton sp. patches. In the season with fewer rodents, they cached seeds close to the original places irrespective of vegetation, while in the season with abundant rodents, they transported seeds further and cached seeds disproportionately in Lysichiton patches. Seeds missed by rodents were larger than seeds that were eaten or that survived. Sasa patches are more suitable for seedlings to establish and a size advantage was observed there, but even small seeds could establish in Lysichiton patches, although seedling success was lower. We concluded that maternal plants of Symplocarpus renifolius increase their reproductive success by having small to middle-sized seeds transported to suitable sites while offering larger seeds as rewards to the transporters. Since the variation in seed mass was not correlated with the biomass per seed of the maternal plant, the large variation in seed mass is considered to have evolved through the density-dependent hoarding by rodents.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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".