South African Invasive Tree: Studies of the Chemical and Biological Profiles of Acacia Decurrens (Wild)
Bibliographic record
Abstract
In this study, we aimed to profile the stem bark of Acacia decurrens biochemically. Extracts obtained by maceration were phytochemically screened, spectroscopically analysed with the aid of UV-visible, FT-IR, GC-MS, and, ICP-OES and subjected to primary biological assay. Spectra obtained from the UV-visible and FT-IR confirm the presence of n ® p* which are characteristic of compounds with oxygenated backbones such as the glycoside, phenols, terpenoids, tannins, and flavonoids. On further analysis, the chromatogram revealed the presence of thirty-five major compounds of which eight bioactive compounds had previously been isolated. The metal profile of the stem bark registered high concentrations of Cr, K, and Fe. Quantitative phytochemical evaluation showed, large amount of tannins (30.87 – 55. 81 mgTAE/g), steroids (13.92-41.2%), and phenols (40.6 - 65.5 mgGAE/g) in all fractions. The ethyl acetate and methanol fractions were found to be rich sources of antibacterial compounds with MIC value of 12.5 μg/mL while the chloroform fraction is a potent antioxidant fraction with IC50 values of 37.00 ± 0.06 g/mL and 42.20 ± 0.72 µg/mL against DPPH and ABTS radicals, respectively. The presence of these secondary metabolites and the hyper-tolerance capacity for metals can be exploited pharmaceutically and for phytoremediation purpose, respectively.
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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".