An Integrated Exploration Approach to the Sishen South Iron Ore Deposit, Northern Cape Province, South Africa, and Its Implication for Developing a Structural and/or Resource Model for These Deposits
Bibliographic record
Abstract
Abstract The high-grade hematite Sishen South iron ore deposit is located due south of Postmasburg on the southern-plunging limb of the Maremane dome in the Northern Cape of South Africa. To develop a structural model that could be used for mineral resource estimation it was crucial to investigate the effects of at least three compressive orogenic episodes and several extensional events on the formation, erosion, and preservation of iron ore on the Maremane dome and surrounding environs. Due to the general paucity of outcrops, much of the work was initially based on the interpretation of ground gravity surveys, Landsat TM images, and surface drill hole information combined with the limited mapping data. Results indicate that iron ore was preserved from erosion by deep, semicircular, troughlike depressions, formed by the interference of the Kheis orogeny, north-trending F2 synclines, and the Lomanian orogeny east-northeast-trending F3 synclines and half grabens formed adjacent to reactivated west-dipping north-south-striking normal faults. Reactivated faults played a pivotal role at Sishen South; sustaining troughlike depressions in which previously formed iron orebodies could be unaffected by subsequent uplift and erosion. As part of the ongoing regional exploration, a large area in the Northern Cape province was covered by an airborne gravity gradiometric survey. Interpretation of the survey data combined with the previous information facilitated the construction of geologic sections spaced at 5-km intervals across the entire survey area. The effects of regional deformation on the deposit, combined with an integrated approach for its exploration, culminated with the compilation of a set of consistently interpreted structural cross sections for each orebody at Sishen South, which were incorporated into three-dimensional solid models and used for confident mineral resources estimation. Without this consistent approach, confidence in the mineral resource estimate was suboptimal. The iron ore deposits of the Northern Cape province of South Africa have previously been considered type examples of ancient supergene deposits and little support has been afforded hydrothermal and/or supergene-modified hydrothermal ore formation processes. Recently, however, some evidence, which is partly discussed in this paper, has been found relating the iron (and manganese) mineralization in this region to structurally controlled hydrothermal fluid flow related to the Kheis orogeny.
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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 | 0.000 | 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 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".