Effects of forest age on fruit composition and removal in tropical bird-dispersed understorey trees
Bibliographic record
Abstract
Abstract: Little is known about how land-use changes affect interspecific interactions such as fruit–frugivore mutualisms. Forest age could affect both fruit sugar concentrations via differences in light availability or disperser abundance, and fruit removal rates via differences in bird and plant community composition. We examined how these two factors are affected by forest age in a Costa Rican rain forest. We compared seven young-secondary forest species, seven old-growth forest species, andMiconia nervosagrowing in both forests. We measured sugar concentrations in fruits and manipulated the location of paired fruiting branches, measuring subsequent fruit removal. Sugar concentration means were on average 2.1 percentage points higher in young-secondary forest species than in old-growth forest species, but did not differ amongMiconia nervosafruits from the two forests. Fruit removal rates were higher in young-secondary forest for 86% of young-secondary forest species, 71% of old-growth forest species, and on average for both young-secondary and old-growth forestMiconia nervosaindividuals. Higher sugar concentrations in young-secondary forest plants could reflect stronger competition for dispersers, while experimental fruit removal results suggests the opposite patterns of competition; fruits are more likely to be removed by dispersers in young-secondary forest independent of fruit nutrient concentration.
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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 | 0.001 |
| 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".