Floral reward production is timed by an insect pollinator
Bibliographic record
Abstract
Interval timing--sensitivity to elapsing durations--has recently been found to occur in an invertebrate pollinator, the bumble-bee (Bombus impatiens). Here, bumble-bees were required to time the interval between the start of foraging in a patch of low-quality artificial flowers providing 25% sucrose and the availability of a high-quality flower (HQF) that provided 50% sucrose after a fixed delay. The delay changed after every 20 foraging bouts in the order 30-150-30 s. Bees visited the HQF sooner when the delay was 30 s than when it was 150 s, and visits to the HQF peaked near the end of both delays. When the delay changed to 150 s, bees appeared to time both the previous 30 s delay and the new delay. To examine whether bees also learned what kind of reward was provided at the HQF, its usual reward was replaced with 25% sucrose in a final foraging bout. Bumble-bees rejected the HQF on the reward-replacement test. These results show that bumble-bees remembered both when reward was produced by the HQF and what type of reward was produced. These findings indicate that bumble-bees can learn both the timing and content of reward production.
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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 | 0.001 | 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".