Applications in geological monitoring: paleoseismology and paleoclimatology
Bibliographic record
Abstract
Key points Coastal ecosystem knowledge is essential for understanding earthquake mechanics and forecasting catastrophic shoreline movement and flooding; multiple sources of fossil proxy-data – including microfossils, pollen and sediment – are best used to reconstruct patterns of earthquakes and tsunamis in time and space; multidisciplinary studies are also needed to distinguish tsunami from tropical storm events; correct measurement of timing and speed of paleoseismic events depend on accurate dating methods, best provided by tree roots and salt marsh peat; foraminifera provide the most precise estimates for amounts of vertical shoreline change; pollen of mangroves and salt marsh plants provide best estimates of climate change; diatom and dinoflagellate paleotransfer functions are best for tracking the prehistoric sea-ice changes. How wetland archives are used in paleoseismology and paleotempestology The past is all we know about the future . (Barbara Kingsolver, The Lacuna , 2009) In Chapters 3 and 4, we explained how study of foraminifera (Box 4.1 Tidal wetland foraminifera) and pollen grains (Figure 3.3) in present-day tidal wetlands can be used to analyse and interpret geological archives of past changes in sea level, salinity and coastal vegetation. Barlow et al . (2013, p. 90) state that, ‘Understanding late Holocene to present relative sea level changes at centennial or subcentennial scales requires geological records that dovetail with the instrumental era. Salt marsh sediments are one of the most reliable geological tide gauges.’ Here we give additional examples of other microfossils and geochemical tracers that can be used as proxies in studies of coastal wetlands, and we describe various case histories for applications in paleoseismology, which is the study of prehistoric earthquakes and tsunamis – particularly their location in space and time. Paleotempestology is the related study of storms and hurricanes from a primarily geological perspective (Liu, 2004, 2007).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.053 | 0.015 |
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".