Identification of Plant Species for Crop Pollinator Habitat Enhancement in the Northern Prairies
Bibliographic record
Abstract
Wild pollinators have a positive impact on the productivity of insect-pollinated crops. Consequently, landowners are being encouraged to maintain and grow wildflower patches to provide habitat for important pollinators. Research on plant-pollinator interaction matrices indicates that a small number of “core” plants provide a disproportionately high amount of pollen and nectar to insects. This matrix data can be used to help design wildflower plantings that provide optimal resources for desirable pollinators. Existing interaction matrices from three tall grass prairie preserves in the northern prairies were used to identify core plant species that are visited by wild pollinators of a common insect-pollinated crop, namely canola (Brassica napus L.). The wildflower preferences of each insect taxon were determined using quantitative insect visitation and floral abundance data. Phenology data were used to calculate the degree of floral synchrony between the wildflowers and canola. Using this information I ranked the 41 wildflowers that share insect visitors with canola according to how useful they are for providing pollinators with forage before and after canola flowers. The top five species were smooth blue aster (Symphyotrichum laeve (L.) A. & D. Löve), stiff goldenrod (Solidago rigida L.), wild bergamot (Monarda fistulosa L.), purple prairie-clover (Dalea purpurea Vent.) and Lindley’s aster (Symphyotrichum ciliolatum (Lindl.) A. & D. Löve). By identifying the most important wild insects for crop pollination, and determining when there will be “pollen and nectar gaps”, appropriate plant species can be selected for companion plantings to increase pollinator populations and crop production.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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) | 0.001 | 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".