Sugar concentration in nectar: a quantitative metric of crop attractiveness for refined pollinator risk assessments
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
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.
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.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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