Insights on the formation of layered ferromanganese precipitates from the southern Mariana Arc, West Pacific, from micron-scale major, minor, and trace element variations
Bibliographic record
Abstract
A combined laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS) and energy dispersive X-ray spectroscopy (EDS) study was used to map 49 elements in four Fe Mn precipitates produced from three different genetic processes (hydrogenetic, hydrothermal, and mixed-type hydrogenetic-hydrothermal) in samples obtained from the southern Mariana Arc. Results show Mn-oxide minerals are consistently found to be associated with Ba, Mo, Sb, V, Zn, and the rare earth elements and yttrium (REY), excluding Ce, whereas Fe-oxyhydroxide minerals are primarily associated with Ti, Co, Cr, Cu, Nb, and Pb. Element distributions in Fe Mn precipitates from the southern Mariana Arc differ from deposits formed in other environments; notably, there is a decoupling of As and Sb, and redox-sensitive elements are more variable than non-redox-sensitive elements. Samples previously classified as either hydrogenetic or hydrothermal in origin, based on bulk geochemical data, show characteristics of both at higher resolution. Combining high resolution in-situ data with genetic classification discrimination diagrams reveals more ambiguity in metal and metalloid origin than previously thought. Mariana Arc Fe Mn precipitates can be separated into two formation sub-types (hydrothermal or mixed hydrothermal/hydrogenetic) based on textural and compositional differences, e.g., differences in Ce/Ce and Y/Ho values and relative variations in (Zr + Y + Ce) versus (Co + Ni) versus (Mn + Fe). Improved classification and knowledge of how fast-growing hydrothermal Fe Mn precipitates form may help us identify a relatively renewable critical metal resource. • High resolution laser ablation maps (∼ 1 mm 2 ) were constructed from Mn crusts from the southern Mariana Arc. • Within each map, variation in composition is highly varied and, in some cases, different from whole rock analyses. • Results show that sources of metals to Mn crusts is highly variable in time and space. • Classification diagrams based on whole rock analyses do not reflect the true variation in metal sources to Mn crusts.
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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) | 0.007 | 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".