Preface to the Focus Section on Historical Seismograms
Bibliographic record
Abstract
Research Article| April 15, 2020 Preface to the Focus Section on Historical Seismograms Allison L. Bent; Allison L. Bent * 1Natural Resources Canada, Ottawa, Ontario, Canada *Corresponding author: allison.bent@canada.ca Search for other works by this author on: GSW Google Scholar Diane I. Doser; Diane I. Doser 2Department of Geological Sciences, University of Texas at El Paso, El Paso, Texas, U.S.A. Search for other works by this author on: GSW Google Scholar Lorraine J. Hwang Lorraine J. Hwang 3University of California, Davis, Davis, California, U.S.A. Search for other works by this author on: GSW Google Scholar Seismological Research Letters (2020) 91 (3): 1356–1358. https://doi.org/10.1785/0220200112 Article history first online: 15 Apr 2020 Cite View This Citation Add to Citation Manager Share Icon Share Facebook Twitter LinkedIn MailTo Tools Icon Tools Get Permissions Search Site Citation Allison L. Bent, Diane I. Doser, Lorraine J. Hwang; Preface to the Focus Section on Historical Seismograms. Seismological Research Letters 2020;; 91 (3): 1356–1358. doi: https://doi.org/10.1785/0220200112 Download citation file: Ris (Zotero) Refmanager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu toolbar search search input Search input auto suggest filter your search All ContentBy SocietySeismological Research Letters Search Advanced Search Seismology is now in an era where high‐quality digital recordings are abundant and easily accessed. The digital era, however, encompasses only a small fraction of recorded seismic history. The predigital era with recordings on paper, tape, and other media covers a much longer time period. Data collected during this time are important not only in the study of earthquakes and related phenomena but also under new paradigms. Synthesizing data and information from the predigital era with modern data and analysis techniques presents new opportunities for discovery and learning. However, these predigital seismograms, which comprise a vast, largely undertapped data source,... You do not have access to this content, please speak to your institutional administrator if you feel you should have access.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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 teacher head, 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".