Bibliographic record
Abstract
Research in pollination biology has focused on the interactions between animals and the flowers they visit for food reward. However, other selective agents, including predators, seed feeders and herbivores, may affect pollination systems. Because flowers are predictable food sources for a variety of species, flowers are also reliable sites at which predators can locate flower-visiting animals. Prominent among pollinators' predators are beewolves (Philanthus spp), common sphecid wasps (Sphecidae) that prey almost exclusively on bees. My field work over three years indicates, first, that an area of approximately 50 square km surrounding a single bumblebee wolf (Philanthus bicinctus) aggregation had a low bumblebee (Bombus spp) density caused by intense predation by the wasps, and, second, that fruit set of the bumblebee pollinated western monkshood (Aconitum columbianum) was significantly lower at locations and times of bumblebee wolf activity than at control locations and times with no such predatory activity. These results indicate that predation can sometimes alter plantpollinator interactions.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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".