Flight control and landing precision in the nocturnal bee Megalopta is robust to large changes in light intensity
Bibliographic record
Abstract
Like its diurnal relatives, Megalopta genalis use visual information to control flight. Unlike their diurnal relatives, however, they do this at extremely low light intensities. Although Megalopta has developed optical specialisations to increase visual sensitivity, theoretical studies suggest that this enhanced sensitivity does not enable them to capture enough light to use visual information to reliably control flight in the rainforest at night. It has been proposed that Megalopta gain extra sensitivity by summing visual information over time. While enhancing the reliability of vision, this strategy would decrease the accuracy with which they can detect image motion - a crucial cue for flight control. Here, we test this temporal summation hypothesis by investigating how Megalopta's flight control and landing precision is affected by light intensity and compare our findings with the results of similar experiments performed on the diurnal bumblebee Bombus terrestris, to explore the extent to which Megalopta's adaptations to dim light affect their precision. We find that, unlike Bombus, light intensity does not affect flight and landing precision in Megalopta. Overall, we find little evidence that Megalopta uses a temporal summation strategy in dim light, while we find strong support for the use of this strategy in Bombus.
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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".