Sugar concentration in nectar: a quantitative metric of crop attractiveness for refined pollinator risk assessments
Bibliographic record
Abstract
Those involved with pollinator risk assessment know that agricultural crops vary in attractiveness to bees. Intuitively, this means that exposure to agricultural pesticides is likely greatest for attractive plants and lowest for unattractive plants. While crop attractiveness in the risk assessment process has been qualitatively remarked on by some authorities, absent is direction on how to refine the process with quantitative metrics of attractiveness. At a high level, attractiveness of crops to bees appears to depend on several key variables, including but not limited to: floral, olfactory, visual and tactile cues; seasonal availability; physical and behavioral characteristics of the bee; plant and nectar rewards. Notwithstanding the complexities and interactions among these variables, sugar content in nectar stands out as a suitable quantitative metric by which to refine pollinator risk assessments for attractiveness. Provided herein is a proposed way to use sugar nectar concentration to adjust the exposure parameter (with what is called a crop attractiveness factor) in the calculation of risk quotients in order to derive crop-specific tier I assessments. This Perspective is meant to invite discussion on incorporating such changes in the risk assessment process. © 2016 The Authors. Pest Management Science published by John Wiley & Sons Ltd on behalf of Society of Chemical Industry.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".