Assessment of the Potential of Umbrella Thorn [Vachellia tortilis (Forssk.) Galasso & Banfi] for the Rehabilitation of Sub-Sahelian Mining Sites at Essakane, North-Eastern Burkina Faso
Bibliographic record
Abstract
Establishment of plant communities at mine sites with significant disturbance depends upon factors such as the presence of efficient mycorrhizal fungi and rhizobia. Field and greenhouse experiments were conducted to assess growth performance of umbrella thorn as a potential tree species for reforestation of mine sites in arid areas. In the first experiment, seedlings were transplanted onto waste rock stockpile (WR) and tailings (TLG) with high arsenic (As) content on sites at the Essakane gold mine. Trees were assessed for growth and survival 3 years after planting. In experiment 2, soil was sampled on four sites across a disturbance gradient from mining waste (WR and TLG) and artisanal gold mining to undisturbed natural soil (NS). Each soil was planted with two separate leguminous trap crops (cowpea and umbrella thorn) in pots to check for microsymbionts. At 3 years, trees grew better on TLG soils with greater arsenic contamination than WRs in the field. Although tree establishment was moderate, with <50% survival, overall results suggest the ability of umbrella thorn to tolerate As contamination levels up to 1,300 ppm and, therefore, its potential for reforestation. Soil pH has shown strong effects on soil nutrient content. In particular, ammonium was the dominant form of mineral nitrogen (N) in the more neutral pH NS soils, while nitrate was present in the more alkaline WRs. Denitrification likely resulted in high N loss where nitrate dominates, reflecting the poor performance of N-deficient trees on WRs compared to TLG soils. Growth trends of umbrella thorn in potted-soils were consistent with those reported on TLG and WR soils in plantations.
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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) | 0.000 | 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".