Palaeoseismology: Historical and Prehistorical Records of Earthquake Ground Effects for Seismic Hazard Assessment
Bibliographic record
Abstract
Book Review| August 01, 2012 Palaeoseismology: Historical and Prehistorical Records of Earthquake Ground Effects for Seismic Hazard Assessment K. Reicherter; K. Reicherter Director, Centre for Natural Hazard Research, Department of Earth Sciences, Simon Fraser University, 8888 University Drive, Burnaby, BC, Canada V5A 1S6 Search for other works by this author on: GSW Google Scholar A.M. Michetti; A.M. Michetti Director, Centre for Natural Hazard Research, Department of Earth Sciences, Simon Fraser University, 8888 University Drive, Burnaby, BC, Canada V5A 1S6 Search for other works by this author on: GSW Google Scholar P.G Silva P.G Silva Director, Centre for Natural Hazard Research, Department of Earth Sciences, Simon Fraser University, 8888 University Drive, Burnaby, BC, Canada V5A 1S6 Search for other works by this author on: GSW Google Scholar Environmental and Engineering Geoscience (2012) 18 (3): 309–310. https://doi.org/10.2113/gseegeosci.18.3.309 Article history first online: 02 Mar 2017 Cite View This Citation Add to Citation Manager Share Icon Share Twitter LinkedIn Tools Icon Tools Get Permissions Search Site Citation K. Reicherter, A.M. Michetti, P.G Silva; Palaeoseismology: Historical and Prehistorical Records of Earthquake Ground Effects for Seismic Hazard Assessment. Environmental and Engineering Geoscience 2012;; 18 (3): 309–310. doi: https://doi.org/10.2113/gseegeosci.18.3.309 Download citation file: Ris (Zotero) Refmanager EasyBib Bookends Mendeley Papers EndNote RefWorks BibTex toolbar search Search Dropdown Menu nav search search input Search input auto suggest search filter All ContentBy SocietyEnvironmental and Engineering Geoscience Search Advanced Search Review by: John J. Clague Earthquakes are one of the greatest natural hazards humans face. In the 20th century alone, over two million people have died during strong earthquakes and in the fires, tsunamis, and landslides that accompanied them. It is unlikely that the death toll in this century will be any lower, as evidenced by the tragic toll of the earthquakes in Pakistan in 2005, China in 2008, and Haiti in 2010, as well as the earthquake-triggered tsunamis in the Indian Ocean in 2004 and Japan in 2011. Until better construction practices are adopted and proactive earthquake preparation becomes... You do not currently have access to this article.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.009 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.020 | 0.014 |
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".