The onset of precipitation mediates plant–avian disperser interaction in recalcitrant seeds: the case of<i>Cryptocarya alba</i>(MOL) Looser, in Mediterranean ecosystems, Central Chile
Bibliographic record
Abstract
Background: Arid and semi-arid environments impose limitations on plant regeneration. This is especially important during biotic dispersal in plants with recalcitrant seeds, in relation to when and where birds disperse these seeds. Aims: We examined the effect of the onset of seasonal precipitation on the regeneration of Cryptocarya alba (Lauraceae), comparing seedling recruitment during early and late onset of precipitations. Methods: We conducted field experiments using bird-dispersed and gravity-dispersed seeds; half of the seeds were placed during the initiation of precipitations; the other half was kept, then was placed in the forest ground 2 months later. We compared seedling recruitment probability among treatments. Results: Seedling recruitment was positively affected by avian dispersal. Seedling recruitment was significantly increased only for the fraction of bird-dispersed seeds that matched with the beginning of precipitations. Conclusions: The ecological context as well as seed recalcitrance are critical in determining the fate of bird-dispersed seeds. Although this trait may be not adaptive in seasonal ecosystems, it is correlated with other traits that are adaptive (seed size, directed dispersal, spread of the dispersal season), thus explaining the persistence of species with recalcitrant seeds in such environments.
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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".