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Record W2991622777 · doi:10.1002/jqs.3169

Standard chemical‐based tephra extraction methods significantly alter the geochemistry of volcanic glass shards

2019· article· en· W2991622777 on OpenAlexaff
Claire L. Cooper, Ivan P. Savov, Graeme T. Swindles

Bibliographic record

VenueJournal of Quaternary Science · 2019
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsCarleton University
FundersUniversity of LeedsQuaternary Research Association
KeywordsTephraGeologyTephrochronologyGeochemistryVolcanic glassRhyoliteFelsicVolcanoBasaltVolcanic ashExtraction (chemistry)Volcanic rockMineralogyChemistry

Abstract

fetched live from OpenAlex

ABSTRACT The chemical compositions of tephra shards are widely utilised in a myriad of disciplines, including volcanology, petrology, tephrochronology, palaeoecology and climate studies. Previous research has raised concerns over the possible chemical alteration of microscopic (<100 µm) volcanic glass shards through standard extraction procedures, such as the widely used acid digestion method. This study subjects 10 samples of well‐characterised volcanic glasses ranging from basalt to rhyolite to three common methods used in the extraction of volcanic material from lake sediments and peats. The major element geochemistry of each sample was analysed and compared with a control group. The results of this test indicate that basaltic and andesitic glasses are highly susceptible to chemical alteration, particularly to the concentrated corrosive materials used in acid and base digestion techniques. PERMANOVA analysis of the variation within groups suggests that the oxides most susceptible to variation are alkalis from groups I and II (K 2 O, Na 2 O, CaO, MgO) and SiO 2 , and the most stable oxides are Al 2 O 3 and FeO. Felsic glasses are considerably less susceptible to alteration by both acidic (HCl, HNO 3 , H 2 SO 4 ) and alkaline (KOH) digestions. Our findings have important implications for interpreting the geochemistry of volcanic glasses. Copyright © 2019 John Wiley & Sons, Ltd.

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)0.005
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 score0.388
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.023
GPT teacher head0.331
Teacher spread0.308 · 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

Citations12
Published2019
Admission routes1
Has abstractyes

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