Management of Large Seismic Datasets: I. Automated Building and Updating using BREQ_FAST and NetDC
Bibliographic record
Abstract
Research Article| March 01, 2011 Management of Large Seismic Datasets: I. Automated Building and Updating using BREQ_FAST and NetDC 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): 211–221. https://doi.org/10.1785/gssrl.82.2.211 Article history First Online: 09 Mar 2017 Cite View This Citation Add to Citation Manager Share Icon Share Facebook Twitter LinkedIn MailTo Tools Icon Tools Get Permissions Search Site Citation Igor B. Morozov, Gary L. Pavlis; Management of Large Seismic Datasets: I. Automated Building and Updating using BREQ_FAST and NetDC. Seismological Research Letters 2011;; 82 (2): 211–221. doi: https://doi.org/10.1785/gssrl.82.2.211 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 Together with its companion paper (Part II, this issue), this article describes an automated procedure for building, maintaining, and analyzing large and complex earthquake datasets. We present the general concept of a “process-centric” approach to seismic data handling and its implementation based on combining a general-purpose, high-performance seismic processing system with open-source SQL databases. By forming requests for waveforms and metadata, sending them to the Incorporated Research Institutions for Seismology (IRIS) Data Management Center (DMC), and loading the resulting files in various formats, large datasets can be efficiently compiled... 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 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".