The behavior of Fe isotopes in Fe skarns: A case study from the Yeshan Fe skarn deposit, Eastern China
Bibliographic record
Abstract
• The δ 56 Fe values exhibit a narrow variation in different skarn zones, particularly in garnet endoskarn and garnet exoskarn. • The δ 56 Fe values of garnet and garnet-diopside skarn are mainly controlled by mineralogy, with garnet skarn having higher δ 56 Fe values than garnet-diopside skarn. • Skarn garnet could record the δ 56 Fe values of hydrothermal fluids. To characterize the applicability of Fe isotopes to skarn petrogenesis and exploration, a robust understanding of their fractionation behavior during skarn alteration and mineralization is required. Here, we characterize the bulk-rock Fe isotope composition of endo- and exoskarn, magnetite ore, and the intrusive rocks that comprise the Yeshan Fe skarn deposit in Eastern China, aiming at better constraining the behavior of Fe isotopes during skarn formation and mineralization. The δ 56 Fe values of garnet and garnet–diopside skarn (–0.19 ‰ to 0.15 ‰, n = 15) are negatively correlated with bulk-rock MgO/Al 2 O 3 , suggesting that the variation in δ 56 Fe of these samples is mainly controlled by mineralogy. Garnet skarn is characterized by δ 56 Fe values (0.07 ‰ to 0.15 ‰, n = 11) that are similar to those of the spatially associated quartz monzonite pluton (0.12 ‰ to 0.16 ‰, n = 2), and it has systematically higher Fe 2 O 3 contents. Based on petrological observations and bulk-rock geochemistry (e.g., REE and P 2 O 5 contents), garnet, the main Fe-bearing mineral in the garnet skarn, is inferred to have achieved equilibrium (or near equilibrium) with the hydrothermal fluids that circulated throughout the mineralized system, implying that the δ 56 Fe values of garnet skarn can be used to trace the δ 56 Fe values of the fluids. Epidote skarn has a similar Fe 2 O 3 content as garnet skarn, but lower δ 56 Fe values (–0.07 ‰ to 0.01 ‰, n = 2). Epidote skarn was genetically associated with low temperature, FeCl 2 (H 2 O) 4 -dominated fluids, which are in contrast with that (high-temperature, [FeCl 4 ] 2- -dominated fluids) formed the garnet skarn. Iron in diopside skarn is mainly hosted by magnetite; the δ 56 Fe values of this lithology (0.00 ‰ to 0.12 ‰, n = 2) are, therefore, mainly controlled by magnetite. Taken together, the Fe isotopic signatures of various types of skarn at Yeshan could enhance our understanding of skarn deposits worldwide, especially those sharing geological features similar to the Yeshan Fe skarn deposit.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".