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Record W2890018267 · doi:10.1785/0220180258

Earthquake Myths and False Information: How to Respond While Avoiding a Mud Fight

2018· article· en· W2890018267 on OpenAlexaffabout
Maurice Lamontagne, Christine Goulet

Bibliographic record

VenueSeismological Research Letters · 2018
Typearticle
Languageen
FieldSocial Sciences
TopicDisaster Management and Resilience
Canadian institutionsGeological Survey of Canada
Fundersnot available
KeywordsMythologyGeologySeismologyForensic engineeringComputer securityMining engineeringComputer scienceEngineeringHistory

Abstract

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Research Article| September 19, 2018 Earthquake Myths and False Information: How to Respond While Avoiding a Mud Fight Maurice Lamontagne; Maurice Lamontagne aGeological Survey of Canada, Ottawa, Ontario, Canada K1A 0E9, maurice.lamontagne@canada.ca Search for other works by this author on: GSW Google Scholar Christine Goulet Christine Goulet bSouthern California Earthquake Center, University of Southern California, Los Angeles, California 90034 U.S.A., cgoulet@usc.edu Search for other works by this author on: GSW Google Scholar Author and Article Information Maurice Lamontagne aGeological Survey of Canada, Ottawa, Ontario, Canada K1A 0E9, maurice.lamontagne@canada.ca Christine Goulet bSouthern California Earthquake Center, University of Southern California, Los Angeles, California 90034 U.S.A., cgoulet@usc.edu Publisher: Seismological Society of America First Online: 19 Sep 2018 Online Issn: 1938-2057 Print Issn: 0895-0695 © Seismological Society of America Seismological Research Letters (2018) 89 (6): 2411–2412. https://doi.org/10.1785/0220180258 Article history First Online: 19 Sep 2018 Cite View This Citation Add to Citation Manager Share Icon Share Facebook Twitter LinkedIn MailTo Tools Icon Tools Get Permissions Search Site Citation Maurice Lamontagne, Christine Goulet; Earthquake Myths and False Information: How to Respond While Avoiding a Mud Fight. Seismological Research Letters 2018;; 89 (6): 2411–2412. doi: https://doi.org/10.1785/0220180258 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 Earthquake misconceptions and myths are plentiful and can sometimes surprise seismologists (see Table 1). We have all heard poorly documented stories about strange animal behavior before an earthquake, the (usually postearthquake) predictions from psychics about imminent earthquakes, or the enduring myth that people can fall into a chasm when the earth opens up during an earthquake, only to be crushed when it closes again. A web search can reveal numerous examples of these misconceptions, some of them corrected on the U.S. Geological Survey (USGS, 2018) or Earthquake Country Alliance (2011) websites. Interestingly, lessons of the past remind... You do not have access to this content, please speak to your institutional administrator if you feel you should have access.

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)0.004
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.339
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.083
GPT teacher head0.366
Teacher spread0.283 · 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
GenreEmpirical

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

Citations1
Published2018
Admission routes2
Has abstractyes

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