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Record W4393436843 · doi:10.5281/zenodo.3866267

Best practice templates for tephra collection, analysis, and correlation

2020· dataset· en· W4393436843 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

VenueZenodo (CERN European Organization for Nuclear Research) · 2020
Typedataset
Languageen
FieldComputer Science
TopicSpeech and dialogue systems
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsTephraTemplateComputer scienceGeologyPaleontologyVolcanoProgramming language

Abstract

fetched live from OpenAlex

Tephra is a unique volcanic product that has played an unparalleled role in the understanding of past eruptions, long-term behavior of volcanoes, effects of volcanism on climate and the environment. It provides spatially widespread, extremely high-resolution geologic time markers<strong>.</strong> We present comprehensive tephra domain-specific improvements in the data gathering framework, consisting of standardized field data collection, laboratory analysis, and correlation recommendations developed as templates. This new approach will facilitate better tephra documentation and parametrization, increased and improved communication, and more effective data sharing among diverse and to some degree disconnected scholarly communities. We hope that this will result in a synergism that benefits all researchers who work with tephra. This new standardized framework is critical to unlocking future advances that are emerging in tephrochronology, to more completely characterize eruptions and tephra transport, and to exploring the nature of potentially global-scale time-stratigraphic markers. For paleoclimatology, archeology, societal volcanic risk assessment, and volcanogenic environmental impact research, the framework will provide tools for consistent evaluation of tephra deposits on a global scale and across scientific disciplines.

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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.225
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.000
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.004

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 head0.029
GPT teacher head0.261
Teacher spread0.232 · 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 designNot applicable
Domainnot available
GenreDataset

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

Citations0
Published2020
Admission routes1
Has abstractyes

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