Effective dispersal of large seeds by Baird's tapir: a large-scale field experiment
Bibliographic record
Abstract
Even though the full process of seed dispersal is the combination of movement mode and distance, deposition, successful germination and survival (Nathan 2006, Westcott et al. 2005), relatively few studies have documented the role of mammals as facilitators of germination and survival (Paine & Harms 2009). In particular, the effectiveness of large terrestrial mammals (>50 kg) as effective dispersers of large seeds is poorly known, but has been linked to the treatment of the seeds in their digestive system, the deposition of viable seeds in nutrient-rich environments (faeces) and favourable sites. Other aspects related to long-distance movements, defecation patterns and home-range size are frequently cited as factors that favour the deposition of seeds far from parent trees, which is expected to reduce predation and intraspecific competition, and enhance fitness (Schupp et al. 2002). We addressed these issues through a large-scale field experiment.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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 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".