Spatial pattern and size distribution of the animal-dispersed tree<i>Quercus robur</i>in two spruce-dominated forests
Why this work is in the frame
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Bibliographic record
Abstract
We investigated the degree to which the spatial distribution of oaks (Quercus robur L.) was related to habitat conditions, as reflected by vegetation type and structural features presumed to attract animal dispersers (trails, community borders). We hypothesized that the distribution pattern of oaks, with their potential to establish in many habitats, depends on the behaviour of the dispersing animals to a greater extent than micro-habitat conditions. One 100 m ¥ 100 m plot was surveyed in each of two coniferous forests in east-central Sweden. No adult oak trees grew in the forests; all oaks were considered as dispersed into the plots by animals. We tested whether oak distribution was clumped with spatial autocorrelation analyses and whether oak distribution was related to vegetation type, species composition, tree cover, distance to nearest fertile oak tree, or distance to animal trails. Our study showed that oak trees were also spatially aggregated in a small-scale context. The spatial distribution of seedlings and older trees were associated with species richness and tree cover but not with any specific vegetation type, even though fewer oaks than expected grew in spruce forest habitats. Furthermore, we found that oak trees were associated with trails. There were differences in oak distribution between the two study sites in total number of oaks, the number of first-year seedlings, caches, and oak occurrence in relation to species richness and distance to nearest fertile oak. Seed-dispersing animals seem to be of importance for oak distribution even though animal activities seem to differ between sites.
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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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it