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Record W7116848290 · doi:10.1201/9781003761259-30

Tsunami deposit mapping at Seaside, Oregon, USA

2025· book-chapter· en· W7116848290 on OpenAlexaboutno aff
Brooke Fiedorowicz, Curt D. Peterson

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldEarth and Planetary Sciences
Topicearthquake and tectonic studies
Canadian institutionsnot available
Fundersnot available
KeywordsMargin (machine learning)WetlandPrehistoryRadiocarbon datingHistorical record

Abstract

fetched live from OpenAlex

A considerable amount of research has been conducted during the past decade to establish evidence for prehistoric earthquakes along the Cascadia margin of the United States and Canada. Distinct sediments, deposited along the low-lying coastal wetlands as a result of tsunami inundation, have provided a geologic record of large earthquakes along the Cascadia margin ( Atwater et al. 1995 ). Mapping of paleotsunami deposits along the coastal wetlands of the Pacific Northwest has been used to correlate prehistoric and historic earthquakes, to establish inland distances of high-velocity surge inundation and to estimate open-coast runup heights from barrier overtopping. An accurate characterization of these parameters depends on local geomorphology and the preservation and identification of distinctive tsunami deposits.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.055
Threshold uncertainty score0.109

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0260.005

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 head0.022
GPT teacher head0.201
Teacher spread0.179 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
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

Citations0
Published2025
Admission routes1
Has abstractyes

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