Experience levels of individuals in natural bee populations and their ecological implications
Bibliographic record
Abstract
Learning difficult tasks requires an extended period of experience. It is unclear, however, what level of experience is exhibited by individuals in natural populations. If many individuals are rather inexperienced at any given time, they may not possess subtle information concerning, for example, local distributions of reward and danger, which may require long acquisition periods. To quantify individual experience in field settings, we conducted a field study involving extensive marking of individual honey bees (Apis mellifera L., 1758) and bumble bees (Bombus vagans Smith, 1854 and Bombus terricola Kirby, 1837) visiting milkweed (Asclepias syriaca L.) patches that harbored crab spiders (Misumena vatia (Clerck, 1757)), which prey on bees. The vast majority of bees either were fully inexperienced or had little experience with the specific flower patch that they were visiting. It is likely that such inexperienced bees do not possess subtle local information involving either reward or danger. Contrary to our prediction, even the most experienced bees did not avoid experimental patches harboring crab spiders, perhaps because even these bees did not possess sufficient experience. Our results indicate that conclusions from controlled laboratory experiments may not readily generalize to natural field settings. Thus, we must gather additional data on the long-term behavior of individually marked bees in natural conditions to better understand the interactions among flowers, bees, and bees' predators.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".