Honey authenticity: the opacity of analytical reports - part 1 defining the problem
Bibliographic record
Abstract
The composition of honey, a complex natural product, challenges analytical methods attempting to determine its authenticity particularly in the face of sophisticated adulteration. Of the advanced analytical techniques available, only isotope ratio mass spectrometry (IRMS) is generally accepted for its reproducibility and ability to detect certain added sugars, with nuclear magnetic resonance (NMR) and high-resolution mass spectrometry (HRMS) being subject to stakeholder differences of opinion. Herein, recent reviews of honey adulteration and the techniques to detect it are summarised in the light of which analytical reports are examined that underpinned a media article in late 2020 alleging foreign sugars in UK retailers' own brand honeys. The requirement for multiple analytical techniques leads to complex reports from which it is difficult to draw an overarching and unequivocal authenticity opinion. Thus arose two questions. (1) Is it acceptable to report an adverse interpretation without exhibiting all the supporting data? (2) How may a valid overarching authenticity opinion be derived from a large partially conflicting dataset?
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.
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 | 0.045 | 0.110 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.010 |
| Scholarly communication | 0.009 | 0.014 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.005 | 0.008 |
| Insufficient payload (model declined to judge) | 0.003 | 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".