Determination of Fe<sup>2+</sup>/Fe<sup>3+</sup> Ratios of Magnetite using Different Methods: A Case Study from the Qimantag Metallogenic Belt
Bibliographic record
Abstract
Abstract Determination of Fe2+/Fe3+ ratios from metallogenic belts to explore controlling physical and chemical conditions of rock formation is of great significance. In order to explore magnetite Fe2+/Fe3+ ratios of the Qimantag metallogenic belt, part of the Eastern Kunlun orogenic belt in the northeastern margin of the Qinghai–Tibetan plateau, western Central Orogenic Belt of China, and overcome the limitation of the traditional electronic probe, five different measurement methods are proposed and their respective advantages and disadvantages evaluated, with the composition data of the magnetite obtained using electron probe microanalysis (EPMA). The direct oxygen measurement method has a significant impact on the determination results of FeO and Fe2O3, but the accuracy and uniformity of the results are low. The valence method (Flank method) based on the spectral intensity ratio of Lα to Lβ for iron is also unreliable for FeO and Fe2O3 measurements because it is difficult to establish a relationship between Lβ/Lα, the spectral intensity ratio, and the Fe2+/Fe3+ content ratio. In comparison, the charge difference method, the surplus‐oxygen method and the Mössbauer spectrum method are still the most favorable. Mössbauer spectroscopy, with its isomer movement particularly sensitive to the oxidation state of iron, yields results closer to 0.5, which is relatively reliable. Earlier magnetite deposits are located in intrusions or contact zones and formed by magmatic fluids with high Fe2+/Fe3+ ratios, whereas later magnetite deposits are farther away from intrusions and have low Fe2+/Fe3+ ratios. The transformation mechanism of hematite and magnetite in the Qimantage metallogenic belt is also studied. No large volume changes, such as pore filling and shrinkage fracture, were detected in the metallogenic belt, and the transformation mechanism is more similar to a reoxidation and reduction mechanism.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 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".