Identification of plant species for pollinator restoration in the Northern Prairies
Bibliographic record
Abstract
Research on diurnal plant–pollinator interactions indicates that a small number of generalist plants provide a disproportionately high amount of floral resources to pollinating insects. Identifying these generalist plants would help prairie restoration specialists select species that will provide forage for the majority of pollinator taxa. Field research in three Canadian fescue (Festuca hallii) prairie preserves that were at most 3.3 km away from each other was conducted in 2014 and 2015 to create pooled, weighted, plant–insect visitor matrices for each site. Using these matrices, generalization (G) scores were calculated for each plant species to help assess their importance to wild insect visitors as this method controls for differences in insect abundances over the year. The three species with the highest average generalization scores were Solidago rigida, Erigeron glabellus and Symphyotrichum laeve. Species accumulation curves were created to determine how many plant species would need to be present before most pollinator taxa would have at least one acceptable forage species. This research indicates that the 16 plant species (33% of the total) with the highest average generalization scores were visited by 90% of the observed pollinator taxa. To detect exceptionally attractive plant species while accounting for natural differences in abundance, we calculated the insect, bee and fly visitation rates per inflorescence. There was several specialized plant species that were visited frequently by bees. Most of these specialized plants had purple or yellow, tubular flowers, and bloomed in mid to late summer when bee populations were most numerous.
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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".