Geophysical methods used in the discovery of the Kitumba iron oxide copper gold deposit
Bibliographic record
Abstract
Abstract A range of geophysical techniques has been used at various stages of the discovery and delineation of the Kitumba deposit in Central Zambia. Early era magnetics, geologic mapping, artisanal Cu plays, and the application of an iron oxide copper gold (IOCG) exploration model led explorers to the area in the 1990s. An airborne gravity gradiometer (AGG) survey was flown in 2004, and it highlighted key regional elements considered to be prerequisite for prospective IOCG mineralization. The AGG survey accurately delineated the spatial extents of two target areas referred to as the Kitumba and Mutoya systems. Gravity, radiometric, and magnetic data sets acquired as part of the AGG survey have mapped geologic and structural information as well as the extent of the IOCG alteration system. Significant uranium anomalism in the radiometric data was identified at Kitumba upon which the discovery hole S36-001 was sited. In 2012, a 3D direct current resistivity and induced polarization survey was conducted over Kitumba. The survey results provided 3D models of induced polarization chargeability anomalism and allowed successful delineation of sulfide material within the known deposit. The survey also provided an enhanced understanding of the 3D geometry of the mineralization. This improved understanding allowed a refocusing of drilling activities to best target extensions to existing mineralization.
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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 | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".