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Record W2084358898 · doi:10.1021/es051476r

Volatile Metals and Metalloids in Hydrothermal Gases

2006· article· en· W2084358898 on OpenAlexfundno aff
Britta Planer‐Friedrich, Broder J. Merkel

Bibliographic record

VenueEnvironmental Science & Technology · 2006
Typearticle
Languageen
FieldEnvironmental Science
TopicHeavy metals in environment
Canadian institutionsnot available
FundersNational Research Council CanadaTechnische Universität Bergakademie FreibergStudienstiftung des Deutschen VolkesKorea Institute for International Economic Policy
KeywordsEnvironmental chemistryChemistryMetalloidDissolutionHydrothermal circulationNickelZincDiffusionMineralogyMetalGeology

Abstract

fetched live from OpenAlex

Volatile metals and metalloids were sampled from hot springs, fumaroles, and a hydrothermally influenced wetland in Yellowstone National Park. The sampling was based on diffusion through gas sampling chambers. Collected gases were stabilized by dissolution and oxidation in 1:100 diluted NaOCl. Special procedures were developed to analyze the oxidized samples by GF-AAS and HG-AAS. For ICP-MS, samples had to be blank-corrected for polyatomic isotope interferences, especially by 23Na35Cl+ and 23Na37Cl+ on 58Ni and 60Ni and by 40Ar23Na+ on 63Cu. From the concentrations trapped in solution, net diffusion rates were calculated by Fick's first law. The highest concentrations reached a maximum of 8 g/m3 for volatile silicon. Volatile nickel, tungsten, zinc, copper, and molybdenum, previously only known from anthropogenic sources, occurred naturally in the hydrothermal gases in ranges of tens to hundreds of microg/m3. Replicate measurements indicated significant temporal variations in concentrations, probably the result of complex changes in the hydrothermal regime as well as varying microbial activity. Global correlations between gaseous and superficial aqueous phases were missing.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.423
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.006
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.001

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 head0.004
GPT teacher head0.206
Teacher spread0.201 · 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

Citations29
Published2006
Admission routes1
Has abstractyes

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