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Record W4401559391 · doi:10.1016/j.margeo.2024.107383

Insights on the formation of layered ferromanganese precipitates from the southern Mariana Arc, West Pacific, from micron-scale major, minor, and trace element variations

2024· article· en· W4401559391 on OpenAlexaff
Derek R. Knaack, Matthew I. Leybourne, Andrew M. McDonald, Joseph A. Petrus, Daniel Layton‐Matthews, James R. Hein, Robert J. Stern

Bibliographic record

VenueMarine Geology · 2024
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeochemistry and Elemental Analysis
Canadian institutionsLaurentian UniversityArthur B. McDonald-Canadian Astroparticle Physics Research InstituteQueen's University
Fundersnot available
KeywordsFerromanganeseGeologyTrace elementOceanographyScale (ratio)GeochemistryManganeseMetallurgyMaterials scienceGeography

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.038
Threshold uncertainty score0.994

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.000

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.

Opus teacher head0.007
GPT teacher head0.177
Teacher spread0.170 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations0
Published2024
Admission routes1
Has abstractyes

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