Electrical Properties of Magnetite- and Hematite-Rich Rocks and Ores
Bibliographic record
Abstract
SummaryMagnetite and hematite are common iron-oxides, being found in sedimentary, metamorphic and igneous environments and being associated with a wide variety of deposits styles, including orogenic gold, iron-oxide copper-gold and iron-ore deposits. While the magnetic and mass properties of magnetite and hematite have been comprehensively studied, there is relatively limited published information on their electrical properties, although anecdotally, it would appear that many geophysicists have encountered the situation in which their ‘highly prospective’ EM or IP anomaly has turned out to be the result of barren magnetite, and/or hematite.In 1994, Emerson and Yang extensively studied the electrical properties of magnetite-rich rocks as part of AMIRA project P416. Eight sponsor companies contributed a variety of samples for laboratory measurements of mass, magnetic, galvanic electrical, electromagnetic and induced polarisation properties. A petrological study was also carried out. The electrical properties of hematite have been similarly investigated on behalf of individual companies.This work has demonstrated that sulphide-free, magnetite- and/or hematite-rich rocks can be moderate to good conductors and also exhibit a measurable IP response. And in some cases, electrical anisotropy may be significant. The electrical behaviour of magnetite and hematite is related to factors such as quantity, grain size and texture and their electrical response can be considerably enhanced by relatively small amounts of sulphides, such as chalcopyrite. Field examples are presented confirming laboratory observations.
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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 | 0.001 | 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) | 0.001 | 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".