A Tool for Selecting Plants When Restoring Habitat for Pollinators
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Native pollinators and, particularly bees, are a critical component of agricultural systems. Unfortunately, many factors are leading to their declines, including habitat loss. Consequently, approaches have emerged that aim to restore pollinator habitat in managed landscapes. A widely adopted technique in Europe and North America is the planting of flowering shrubs and forbs along field edges. These habitats usually include a variety of species, chosen because they are attractive to pollinators and because they flower continuously over those pollinators' flight seasons. Because there are many potential plant species with different flowering times and pollinator preferences, selecting a subset is challenging. Here, we develop a tool that identifies a plant mix that optimizes some assessment criteria (e.g., pollinator visitation, richness, or phenology). We test our tool by showing that it identifies mixes that better satisfy these criteria than ones found using conventional expert‐driven methods, when applied to a plant–pollinator dataset.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 it