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Record W4393591672 · doi:10.5281/zenodo.6568306

Community Established Best Practice Recommendations for Tephra Studies-from Collection through Analysis

2022· dataset· en· W4393591672 on OpenAlexaff
Peter M Abbott, Costanza Bonadonna, Marcus Bursik, Katherine Cashman, Siwan M. Davies, Britta J.L. Jensen, Stephen C. Kuehn, Andrei V. Kurbatov, Christine Lane, Gill Plunkett, Vicki Smith, Emma Thomlinson, Thor Thordarsson, J. Douglas Walker, Kristi L. Wallace

Bibliographic record

VenueExplore Bristol Research · 2022
Typedataset
Languageen
FieldSocial Sciences
TopicAsian Geopolitics and Ethnography
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsTephraData scienceComputer scienceBiologyVolcanoPaleontology

Abstract

fetched live from OpenAlex

Tephra is a unique volcanic product with an unparalleled role in understanding past eruptions, long-term behavior of volcanoes, and the effects of volcanism on climate and the environment. Tephra deposits also provide spatially widespread, extremely high-resolution time-stratigraphic markers across a range of sedimentary settings and are used in a range of disciplines (e.g., volcanology, climate science, archaeology, ecology, and impact assessment). Nonetheless, the study of tephra deposits is challenged by a lack of standardization that often inhibits data integration across geographic regions and across disciplines. Here we present comprehensive recommendations for tephra data gathering and reporting that were developed by the tephra science community to serve as guidelines for future investigators and to ensure that sufficient data are gathered for transparency and interoperability. Recommendations include standardized field and laboratory data collection along with reporting and correlation guidance. These are organized as tabulated lists of key metadata with their definition and purpose. They are system independent and usable for template, tool, and database development. This new standardized framework promotes consistent tephra documentation and archiving, fosters interdisciplinary communication, and improves effectiveness of data sharing among diverse communities of researchers. Wider adoption will help to expand the applicability and usability of tephra data and facilitate scientific collaboration and data reuse. For additional details, see the accompanying manuscript: Wallace, K.*, Bursik, M. Kuehn, S., Kurbatov, A., Abbott, P., Bonadonna, C., Cashman, K., Davies, S., Jensen, B., Lane, C., Plunkett, G., Smith, V. Tomlinson, E., Thordarsson, T., and Walker, D. Community established best practice recommendations for tephra studies—from collection through analysis. Sci Data 9, 447 (2022). https://doi.org/10.1038/s41597-022-01515-y *corresponding author: Kristi Wallace, kwallace@usgs.gov Open access article is available online here https://doi.org/10.1038/s41597-022-01515-y or as a PDF here https://www.nature.com/articles/s41597-022-01515-y.pdf.

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 imitation

Not 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.

metaresearch head score (Codex)0.326
metaresearch head score (Gemma)0.562
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesMetaresearch
DomainCandidate signal: Methods · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.674
Threshold uncertainty score0.831

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.3260.562
Meta-epidemiology (narrow)0.0030.004
Meta-epidemiology (broad)0.0040.007
Bibliometrics0.0190.019
Science and technology studies0.0080.010
Scholarly communication0.0210.023
Open science0.0220.020
Research integrity0.0230.024
Insufficient payload (model declined to judge)0.0240.039

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 head0.472
GPT teacher head0.565
Teacher spread0.093 · 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; the direct Gemma label and the distilled Codex classifier agree on what is shown here.

Study designNot applicable
DomainMethods
GenreMethods

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

Citations1
Published2022
Admission routes1
Has abstractyes

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