The concentration and energetic content of floral nectar sugars: calculation, conversions, and common confusions
Bibliographic record
Abstract
The sugar concentration of floral nectar is a key metric for describing nectar composition and a major factor influencing pollinator visitation to flowers. Across pollination biology research there are multiple approaches in use for describing nectar sugar concentration. With these different approaches there are several potential sources of confusion which, if not accounted for, can lead to errors. Further potential for error arises if researchers wish to make comparisons between the energetic content of nectars containing different ratios of sucrose, fructose and glucose. Regardless of whether concentration is measured per mole or per unit mass, the energetic content differs between the hexose sugars (glucose and fructose) and sucrose. Appropriate conversion is needed for direct comparison. Here we address these two issues with the following aims. We consolidate the literature on this topic with examples of the different methods for reporting nectar sugar concentrations, provide insight into potential sources of error, and derive equations for converting between the different ways of expressing sugar concentration for the three primary nectar sugars: sucrose, glucose and fructose. Second, we discuss the relative energetic content of sucrose, glucose, and fructose, and rationalise adjustment of 'energetic value' rather than reporting concentration directly. In this way, we hope to harmonise ongoing work in pollination ecology.
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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 | 0.026 | 0.127 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.008 | 0.012 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.008 | 0.008 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".