DNA barcoding detects floral origin of Indian honey samples
Bibliographic record
Abstract
The unique medicinal and nutritional properties of honey are determined by its chemical composition. To evaluate the quality of honey, it is essential to study the surrounding vegetation where honeybees forage. In this study we used conventional melissopalynological and DNA barcoding techniques to determine the floral source of honey samples collected from different districts of the state of Mizoram, India. Pollen grains were isolated and genomic DNA was extracted from the honey samples. PCR amplification was carried out using universal barcode candidates ITS2 and rbcL to identify the plant species. Furthermore, TA cloning was carried out to screen the PCR amplicon libraries to identify the presence of multiple plant species. Results from both the melissopalynological and DNA barcoding analyses identified almost exactly the same 22 species, suggesting that both methods are suitable for analysis. However, DNA barcoding is easier and widely practiced. Hence, it can be concluded that DNA barcoding is a useful tool in determining the medicinal and commercial value of honey.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| 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 | 0.001 |
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".