Multi-species fruit and seed removal in a tropical deciduous forest in Mexico
Bibliographic record
Abstract
We determined patterns of post-dispersal fruit and seed removal for 11 common native plants, as well as sunflower seeds ( Helianthus annuus L.), within a tropical deciduous forest in Jalisco, Mexico. Removal values were high in Delonyx regia (Bojer) Raf. (90%), Crescentia alata Kunth. (87%), H. annus (81%), Pithecellobium dulces (Roxb.) Benth (81%), Albizia occidentalis Brandegee (80%), Coccoloba barbadensis Jacq. (80%), Recchia mexicana DC. (80%), Caesalpinia pulcherrima (L.) Sw. (79%), Enterolobium cyclocarpum (Jacq.) Griseb. (73%), moderate in Guazuma ulmifolia Lam. (42%) and Celtis iguanaeus Sarg. (44%), and low in Amphipterygium adstringens (Schlechtend.) Schiede ex Standl. (17%). Low removal rates (29%) in experimental patches open only to arthropods suggest that arthropods have a minor role in removing fruits and seeds of these species. Removal values were high in experimental patches open to all potential agents (77%) and semipermeable patches open to forest-dwelling rodents (76%), suggesting that the latter group, the most abundant terrestrial mammal in this forest, was an important agent for removing the fruits and seeds of study plants. Removal values were higher in experimental patches located in tropical deciduous forest than in tropical semideciduous forest, as well as higher in high density (patches of 30 fruits or seeds) than in low density (patches of five fruits or seeds) experimental patches for most study plants. Mice appear to selectively remove and hoard fruits and seeds according to their energy and nutritional content and the presence of secondary metabolites, and from from high-density food patches and preferred habitats. Nonindependent effects of species, habitat, and density suggest that a complex interplay of factors determines fruit and seed removal for the plants examined from the Chamela tropical forest.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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 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".