Compositional and textural analysis of host-rock diamictite matrix at the Kakula copper deposit, Democratic Republic of Congo
Bibliographic record
Abstract
The Kakula deposit is a high-grade sedimentary-rock-hosted Cu deposit (628 Mt, 2.72% Cu indicated resource, 1% cut-off) ~10 km south of the Kamoa deposit (759 Mt, 2.57% Cu indicated resource, 1% cut-off) in the central African copperbelt, Democratic Republic of Congo. Copper-sulphide ore (chalcocite, bornite, chalcopyrite) at Kakula is predominantly disseminated in the fine-grained matrix of clast-poor (≤20% clasts ≥2 mm) subaqueous debrite (diamictite), at the base of the nearly flat-lying midNeoproterozoic “grand conglomérat” (Mwale formation). Scanning electron microscopy was used to document matrix texture and composition to develop a matrix paragenesis, recorded in five phases of the matrix evolution: sedimentation, pre-ore diagenesis, mainore mineralisation, post-ore alteration, and weak tectonism. The ore-zone matrix is porous, up to 12.5%, and consists of clay- to silt-sized muscovite, quartz, chlorite, Kfeldspar, dolomite, and biotite, whereas least-altered matrix, several hundred metres above copper-sulphide mineralisation, consists of clay- to silt-sized quartz, albite, chlorite, K-feldspar, calcite, and dolomite. Copper-sulphide precipitation is contemporaneous with chlorite and biotite (+/- hematite, quartz, and K-feldspar) and fit in a paragenetic sequence between diagenetic pyrite (± Fe-dolomite) and later muscovite. Hematite is ore-stage and most abundant in areas containing chalcocite. Areas of mm- to cm-scale “aligned” matrix (nearly vertical microfabric of aligned, elongated grains) consists of a higher abundance of muscovite, locally elongated copper-sulphides, and a lower concentration of copper than non-aligned matrix, suggesting that copper-sulphide development pre-dated fabric development and that copper-sulphide grains were later dissolved and possibly remobilised. Although determining the original mineralogy and texture of the diamictite matrix is challenging, the depositional matrix characteristics (clast-rich versus clast-poor diamictite) and the availability of reactive agents (e.g., diagenetic pyrite) may have been important controls on copper grade and distribution. More work is required to constrain the absolute timing of mineralisation, which is a major debate at Kakula and Kamoa.
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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 | 0.003 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 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".