Characterization of honeys by melissopalynology and statistical analysis
Bibliographic record
Abstract
We analyzed pollen from 89 honey samples, collected in León and Palencia provinces (NW Spain). According to their pollen spectra, 46 were considered monofloral. The most abundant monofloral honeys were Erica types followed by Castanea, Centaurea, Reseda and Helianthus. One hundred and forty-two different pollen types were recorded, belonging to 47 families. Fifty-five of them reached percentages over 3% in at least one sample, while the other 87 types never exceeded 3% in any of the 89 samples. The families that were present in the highest number of samples were Fabaceae, Rosaceae, Cistaceae and Asteraceae. Plant families that had the highest percentages were Fabaceae, Ericaceae, Asteraceae, and Rosaceae. The pollen types that appeared in most samples were Rubus ulmifolius (73 samples), Cytisus scoparius (70) and Mentha aquatica (62); the pollen types that had the highest relative abundance were Erica arborea, Lotus corniculatus, Cytisus scoparius. The pollen types of the Ericaceae family, Jasione montana, and Lavandula latifolia types could be used as indicators of the origin of honeys among five zones in the area studied. The use of cluster and correlation statistical analyses proved useful in characterizing honey samples from a geographical and botanical point of view. The honey samples were divided into four classes according to the data matrix of presence/absence, and into seven classes according to absolute frequencies of pollen types in the samples. Key words: Honey, palynology, melissopalynology, botanical origin, characterization
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".