A pervasive typographical error in an equation for calculating nectar density is likely to have limited consequences
Bibliographic record
Abstract
Analysis of floral nectar reward often involves converting nectar volume to sugar mass. Nectar density depends on nectar sugar concentration, and has been empirically measured. A quadratic equation which accurately models sucrose density over a range of concentrations was published in a book in 1987. This equation was restated in a publication in 2001, but with a typographical error transposing two digits in the coefficient of the linear term. The incorrect equation has been used in numerous studies since its publication, including in the generation of large datasets. However, the error in density introduced by the incorrect equation is never more than 0.5%, which is greatly smaller than the standard deviation in published floral measurements of nectar volumes and concentrations. The error is therefore likely to have limited consequences, but we recommend using the more accurate equation for future studies. While exploring this error we found several others, some of which affect reported data and some of which are simple typographical errors introduced while preparing a manuscript. We caution authors to be scrupulous in reporting units and equations relating to nectar measurement.
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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 | 0.071 | 0.403 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.007 | 0.013 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.022 | 0.026 |
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".