Management of Large Seismic Datasets: II. Data Center-type Operation
Bibliographic record
Abstract
Research Article| March 01, 2011 Management of Large Seismic Datasets: II. Data Center-type Operation Igor B. Morozov; Igor B. Morozov Department of Geological Sciences University of Saskatchewan 114 Science Place Saskatoon, Saskatchewan S7N 5E2, Canada igor.morozov@usask.ca (I. B. M.) Search for other works by this author on: GSW Google Scholar Gary L. Pavlis Gary L. Pavlis Department of Geological Sciences University of Saskatchewan 114 Science Place Saskatoon, Saskatchewan S7N 5E2, Canada igor.morozov@usask.ca (I. B. M.) Search for other works by this author on: GSW Google Scholar Author and Article Information Igor B. Morozov Department of Geological Sciences University of Saskatchewan 114 Science Place Saskatoon, Saskatchewan S7N 5E2, Canada igor.morozov@usask.ca (I. B. M.) Gary L. Pavlis Department of Geological Sciences University of Saskatchewan 114 Science Place Saskatoon, Saskatchewan S7N 5E2, Canada igor.morozov@usask.ca (I. B. M.) Publisher: Seismological Society of America First Online: 09 Mar 2017 Online ISSN: 1938-2057 Print ISSN: 0895-0695 © 2011 by the Seismological Society of America Seismological Research Letters (2011) 82 (2): 222–226. https://doi.org/10.1785/gssrl.82.2.222 Article history First Online: 09 Mar 2017 Cite View This Citation Add to Citation Manager Share Icon Share Facebook Twitter LinkedIn Email Permissions Search Site Citation Igor B. Morozov, Gary L. Pavlis; Management of Large Seismic Datasets: II. Data Center-type Operation. Seismological Research Letters 2011;; 82 (2): 222–226. doi: https://doi.org/10.1785/gssrl.82.2.222 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 Online material: IGeoS scripts implementing the procedures described in this paper. In Part I of this article (this issue), we described the “process-centric” model for research-oriented processing of large and complex seismic datasets, such as those produced by the USArray. Here, we show how this processing model is extended to remote, Web-based operation. By using this approach, users can remotely perform custom processing on a “data-center” computer; obtain and exchange data, images, and other documents; and seamlessly collaborate with other researchers. Modern seismological datasets are large, heterogeneous, complex in structure, and often distributed across multiple locations. Assembling and managing such... 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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".