Community Established Best Practice Recommendations for Tephra Studies-from Collection through Analysis
Bibliographic record
Abstract
Tephra is a unique volcanic product that plays 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, seismotectonics, climate science, archaeology, ecology, public health and impact assessment). Nonetheless, the study of tephra deposits is challenged by a lack of standardization that often inhibits data integration amongst geographic regions and across disciplines. Here we present comprehensive recommendations for tephra data gathering that were community-developed via an inclusive process. These recommendations will help expand the applicability and usability of tephra data, thereby fostering scientific collaboration and data reuse. Recommendations include standardized field and laboratory data collection and reporting and correlation guidance, developed as tabulated lists of key pieces of information with their definition and purpose. This new standardized framework will facilitate consistent tephra documentation and parametrization, foster interdisciplinary communication, and improve the effectiveness of data sharing among diverse communities of researchers. For additional details, see the accompanying manuscript that will be submitted to Nature Scientific Data in October 2020: 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. (Scientific Data: SDATA-20-01163, in review: 2020). *corresponding author: Kristi Wallace, kwallace@usgs.gov
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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 | 0.365 | 0.617 |
| Meta-epidemiology (narrow) | 0.003 | 0.005 |
| Meta-epidemiology (broad) | 0.004 | 0.008 |
| Bibliometrics | 0.018 | 0.018 |
| Science and technology studies | 0.007 | 0.010 |
| Scholarly communication | 0.023 | 0.027 |
| Open science | 0.021 | 0.024 |
| Research integrity | 0.027 | 0.027 |
| Insufficient payload (model declined to judge) | 0.028 | 0.043 |
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".