Gold in Plant: A Biogeochemical Approach in Detecting Gold Anomalies Undercover- A Case Study at Pelangio Gold Project at Mamfo Area of Brong Ahafo, Ghana
Bibliographic record
Abstract
Many plants have the ability to take up gold from soils and accumulate them in their tissues. Their concentrations and distributions reflect the nearby gold deposits masked by complex regolith. The 50 vegetation samples collected at Pelangio Tepa concession recorded low and subtle gold (Au) concentrations of 0.2 to 10.4 ppb at Pokukrom target, 0.3 to 28.3 ppb at Nfante East target and 0.1 to 1.7 ppb at Subriso target. Each target area had different concentration populations enough to distinguish the anomalous areas from the background contrary to Au-geochemical expressions derived from the gold in soils. So many uncertainties were placed on the soil-Au-geochemistry because the defined anomalies were not strong and generally appear patchy, weak and subtle that led to the assumption of no associated bedrock mineralisation. The gold in plant samples confirmed the Pokukrom anomaly that has been drilled and known to relate to underlying mineralisation. Much better and robust anomaly was defined by the biogeochemical Au data in plants sampled and analysed for Au at Nfante East target and isolated high patchy anomalies were identified at Subriso area. The case study at Pelangio Mamfo project reveals and recommends the significant application of biogeochemistry in mineral exploration particularly in the field of gold prospecting at the regional exploration stage and endorses it as being practically feasible in regolith-dominated terrains where regolith-landform modifications may impact on the true geochemistry in anomaly delineation. Keywords: Biogeochemistry, Regolith-Dominated-Terrain, Plant, Gold, Pelangio
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".