Tree and shrub seed dispersal in pastures: The importance of rainforest trees outside forest fragments
Bibliographic record
Abstract
Abstract Forest recovery in tropical pastures is limited by seed dispersal, mainly because the seed dispersers of woody plants avoid deforested areas. In Los Tuxtlas, Mexico, we fenced in isolated fig trees that had been left to provide shade in pastures. We monitored seed deposition under their canopies over a year and sampled the established vegetation after 3 y. Dispersal distances were estimated for captured seeds and established plants, assuming that the nearest conspecific adult rooted within 75 m of the fig tree was the mother. Seventy tree and shrub species were captured in seed rain, with a cumulative density of 833 seeds·m−2·y−1. After 3 y, 77 species of trees and shrubs had established (density: 4.0 plants·m−2). Seeds < 7 mm in diameter were frequently dispersed over distances greater than 75 m across the pasture. Larger seeds were dispersed over shorter distances and in much lower numbers, but once they had arrived at the isolated fig trees, germination and establishment success was higher tha...
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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 | 0.000 |
| 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) | 0.001 | 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".