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Record W2117476991 · doi:10.5194/bg-8-2977-2011

Biogeochemistry of manganese in ferruginous Lake Matano, Indonesia

2011· article· en· W2117476991 on OpenAlexafffund
CarriAyne Jones, Sean A. Crowe, Arne Sturm, Karla Leslie, Lachlan C. W. MacLean, Sergei Katsev, Cynthia Henny, David A. Fowle, Donald E. Canfield

Bibliographic record

VenueBiogeosciences · 2011
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeochemistry and Elemental Analysis
Canadian institutionsCanadian Light Source (Canada)University of Saskatchewan
FundersSimon Fraser UniversityUniversity of Washington
KeywordsBiogeochemistryManganeseGeologyEnvironmental scienceChemistryOceanography

Abstract

fetched live from OpenAlex

Abstract. This study explores Mn biogeochemistry in a stratified, ferruginous lake, a modern analogue to ferruginous oceans. Intense Mn cycling occurs in the chemocline where Mn is recycled at least 15 times before sedimentation. The product of biologically catalyzed Mn oxidation in Lake Matano is birnessite. Although there is evidence for abiotic Mn reduction with Fe(II), Mn reduction likely occurs through a variety of pathways. The flux of Fe(II) is insufficient to balance the reduction of Mn at 125 m depth in the water column, and Mn reduction could be a significant contributor to CH4 oxidation. By combining results from synchrotron-based X-ray fluorescence and X-ray spectroscopy, extractions of sinking particles, and reaction transport modeling, we find the kinetics of Mn reduction in the lake's reducing waters are sufficiently rapid to preclude the deposition of Mn oxides from the water column to the sediments underlying ferruginous water. This has strong implications for the interpretation of the sedimentary Mn record.

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 score0.045
Threshold uncertainty score0.996

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.000

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 head0.021
GPT teacher head0.199
Teacher spread0.178 · 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

Citations56
Published2011
Admission routes2
Has abstractyes

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