Welcome to Hotel Hymenoptera: monitoring cavity-nesting bee and wasp distribution and their trophic interactions using community science and metabarcoding
Bibliographic record
Abstract
Abstract Essential ecosystem services are provided by many interactions, including plant-pollinator, predator-prey, and host-parasitoid. These services support food and natural systems through pollination and pest control, however they are challenging to qualify, and previous observational studies may underestimate their complexity. The cavity nesting Hymenoptera are a good example showing all these three interactions and they can be monitored using trap nests. For this study, trap nests were installed at schools across Canada by community scientists to investigate cavity-nesting bee and wasp distributions and interactions. DNA metabarcoding was used to identify the occupants and their food sources. New bee and wasp distributions were found that might be the result of previous under-sampling or recent range expansions. Detailed bipartite and tripartite networks describing species interactions suggest some novel bee, wasp, and parasite associations. These results encourage further investigation into these interactions using molecular methods as detailed range maps and networks provide information to natural historians and conservationists alike.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".