Colour and odour drive fruit selection and seed dispersal by mouse lemurs
Bibliographic record
Abstract
Animals and fruiting plants are involved in a complex set of interactions, with animals relying on fruiting trees as food resources, and fruiting trees relying on animals for seed dispersal. This interdependence shapes fruit signals such as colour and odour, to increase fruit detectability, and animal sensory systems, such as colour vision and olfaction to facilitate food identification and selection. Despite the ecological and evolutionary importance of plant-animal interactions for shaping animal sensory adaptations and plant characteristics, the details of the relationship are poorly understood. Here we examine the role of fruit chromaticity, luminance and odour on seed dispersal by mouse lemurs. We show that both fruit colour and odour significantly predict fruit consumption and seed dispersal by Microcebus ravelobensis and M. murinus. Our study is the first to quantify and examine the role of bimodal fruit signals on seed dispersal in light of the sensory abilities of the disperser.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 0.001 |
| 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 teacher head, 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".