Signal and reward in wild fleshy fruits: Does fruit scent predict nutrient content?
Bibliographic record
Abstract
Plant species with fleshy fruits offer animals rewards such as sugar, protein, and fat, to feed on their fruits and disperse their seeds. They have also evolved visual and olfactory signals indicating their presence and ripeness.In some systems, fruit color serves as a reliable visual signal of nutrient content. Yet even though many volatile chemicals used as olfactory signals derive from nutrients animals seek, it is still unknown whether fruit scent encodes information regarding nutrient content in wild fruits.We examine the relationship between olfactory signals and nutrient rewards in 28 fruiting plant species in Madagascar. We measured the relative amounts of four chemical classes in fruit scent using gas chromatography and mass spectrometry, as well as the relative amounts of sugar and protein in fruit pulp.We found that protein levels are not associated with elevated amounts of chemically related volatile compounds in fruit scent. In contrast, sugar content is strongly associated with the chemical composition of fruit scent.To our knowledge, this is the first research to explore the connection between fruit chemical signals and nutrient rewards. Our results imply that in the case of sugar, fruit scent is predictive of nutrient content and hence an honest signal.
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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".