Wild Pollinators Enhance Fruit Set of Crops Regardless of Honey Bee Abundance
Bibliographic record
Abstract
Honeybees Can't Do It Alone The majority of food crops require pollination to set fruit with the honeybee providing a pollination workhorse, with both feral and managed populations an integral component of crop management (see the Perspective by Tylianakis , published online 28 February). Garibaldi et al. (p. 1608 , published online 28 February) now show that wild pollinators are also a vital part of our crop systems. In more than 40 important crops grown worldwide, wild pollinators improved pollination efficiency, increasing fruit set by twice that facilitated by honeybees. Burkle et al. (p. 1611 , published online 28 February) took advantage of one of the most thorough and oldest data sets available on plant-pollinator interaction networks and recollected data on plant-pollinator interactions after more than 120 years of climate change and landscape alteration. The historical data set consists of observations collected by Charles Robertson near Carlinville, Illinois (USA), in the late 1800s on the phenology of plants and their pollinating insects, as well as information about which plants and pollinators interacted with one another. Many sites were revisited in the early 1970s and in 2009 and 2010 to collect similar plant-pollinator data. Pollinator function has declined through time, with bees showing lower visitation rates and lower fidelity to individual plant species.
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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) | 0.003 | 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".