Essential Oil Content and Composition of Almescla Resin (Protium spruceanum (Benth.) Engl., Burseraceae
Bibliographic record
Abstract
In recent decades, there has been a significant increase in interest in natural therapies. In this way, plants with therapeutic properties constitute an important source of biologically active compounds. The species Protium spruceanum (Benth.) Engl., popularly known as Almescla is widely used in folk medicine, with a wide geographical distribution, often found in gallery forests and palm swamps (veredas), thus easily accessible. The objective of this work was to evaluate the oil yield of the leaves and stem, as well as the chemical composition of Almescla resin collected in a palm swamp in the north-central region of Minas Gerais. After drying, the botanical samples were triturated until a fine powder was obtained and the particle size of the leaf sample standardized through sieves. The test was carried out in triplicate, the oil being obtained from the hot extraction method in Soxhlet with solvent Hexane P.A. The resin was subjected to chromatographic analysis and infrared spectroscopy, by the preparation of KBr pellets (1% m/m). For the leaf sample, an average yield of 3.33% was obtained and for the stem, an average yield of 1.40%. The resin had the major chemical compound α-phellandrene (58.18-71.25%). It is concluded that, Almescla has a great oil yield, and the chemical analysis showed that the species has a great medicinal potential to be exploited.
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 | 0.000 | 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".