Volatiles of <i>Curcuma mangga</i><scp>Val</scp>. & <scp>Zijp</scp> (Zingiberaceae) from Malaysia
Bibliographic record
Abstract
Analysis by GC and GC/MS of the essential oil obtained from Malaysian Curcuma mangga Val. & Zijp (Zingiberaceae) rhizomes allowed the identification of 97 constituents, comprising 89.5% of the total oil composition. The major compounds were identified as myrcene (1; 46.5%) and β-pinene (2; 14.6%). The chemical composition of this and additional 13 oils obtained from selected Curcuma L. taxa were compared using multivariate statistical analyses (agglomerative hierarchical cluster analysis and principal component analysis). The results of the statistical analyses of this particular data set pointed out that 1 could be potentially used as a valuable infrageneric chemotaxonomical marker for C. mangga. Moreover, it seems that C. mangga, C. xanthorrhiza Roxb., and C. longa L. are, with respect to the volatile secondary metabolites, closely related. In addition, comparison of the essential oil profiles revealed a potential influence of the environmental (geographical) factors, alongside with the genetic ones, on the production of volatile secondary metabolites in Curcuma taxa.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".