Geochemistry of Terrestrial Plants in the Central African Copperbelt: Implications for Sediment Hosted Copper-Cobalt Exploration
Bibliographic record
Abstract
Mineral exploration has increasingly targeted areas covered by in situ or transported overburden for shallow to deep-seated orebodies. It remains critical to develop better means to detect the surficial chemical footprint of mineralized areas covered by thick regolith. In such settings, plant geochemistry could potentially be a useful exploration tool, as different plant species have varying degrees of tolerance to metal enrichment in the soil. This review provides insights into the geological and geochemical controls on metal accumulation patterns in soil–plant systems of the Central African Copperbelt. In addition, it highlights the opportunities for integrating the geochemistry of terrestrial plants in emerging exploration technologies, identifies research gaps, and suggests future directions for developing phytogeochemical sampling techniques. This review was conducted using reputable online scholarly databases targeting original research articles published between January 2005 and March 2023, from which selected articles were identified, screened, and used to explore current advances, opportunities, and future directions for the use of plant geochemistry in sediment hosted Cu–Co exploration in the Central African Copperbelt. Various plant species are recognized as ore deposit indicators through either independent phytogeochemistry or complementary approaches. In the Central African Copperbelt, the successful application of hyperaccumulator species for phytoremediation provides the basis for adopting phytogeochemistry in mineral exploration. Furthermore, current advances in remote sensing, machine learning, and deep learning techniques could enable multi-source data integration and allow for the integration of phytogeochemistry.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".