Allelopathic effects of glucosinolate breakdown products in Hanza [Boscia senegalensis (Pers.) Lam.] processing waste water
Bibliographic record
Abstract
Boscia senegalensis is a drought resistant shrub whose seeds are used in West Africa as food. However, the seeds, or hanza, taste bitter which can be cured by soaking them in water for 4-7 days. The waste water resulting from the processing takes up the bitter taste, which makes it unsuitable for consumption. When used for irrigation, allelopathic effects were observed. Glucosinolates and their breakdown products are the potential causes for both the bitter taste and the allelopathic effects. The objectives of this study are to identify and quantify the glucosinolates present in processed and unprocessed hanza as well as different organs of B. senegalensis, to analyze the chemical composition of the processing water, and to pinpoint the causal agent for the allelopathic properties of the waste water. Hanza (seeds without testa), leaves, branches, unripe, and ripe fruits were collected in three populations and subjected to glucosinolate analyses. Methylglucosinolates (MeGSL) were identified in all plant parts and populations, with the highest concentrations being found in the hanza. The levels of MeGSLs in the hanza reduced significantly during the soaking process. Waste water was collected for 6 days and contained large amounts of macro- and micronutrients, MeGSL as well as methylisothiocyanate (MeITC), resulting from the conversion of glucosinolates. Waste water from days 1-3 (High) and 4-6 (Low) was pooled and used to water seeds from 11 different crops to weeds. The High treatment significantly delayed or reduced germination of all the plant species tested. Using similar levels of MeITC as detected in the waste water, we found that germination of a subset of the plant species was inhibited equally to the waste water treatments. This confirmed that the levels of methylisiothiocyanate in the waste water were sufficient to cause the allelopathic effect. This leads to the possibility of using hanza waste water in weed control programs.
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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 | 0.000 | 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".