Standard chemical‐based tephra extraction methods significantly alter the geochemistry of volcanic glass shards
Bibliographic record
Abstract
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 (K2O, Na2O, CaO, MgO) and SiO2, and the most stable oxides are Al2O3 and FeO. Felsic glasses are considerably less susceptible to alteration by both acidic (HCl, HNO3, H2SO4) 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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".