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Record W4417136764 · doi:10.1130/g53721.1

Forest-floor burial in 1507 by the largest Mount Rainier lahar of the past millennium

2025· article· en· W4417136764 on OpenAlexaboutno aff
Bryan A. Black, Patrick T. Pringle, James W. Vallance

Bibliographic record

VenueGeology · 2025
Typearticle
Languageen
FieldEnvironmental Science
TopicLandslides and related hazards
Canadian institutionsnot available
Fundersnot available
KeywordsChronologyLaharMudflowRadiocarbon datingDendrochronologyLandslideVolcanoMount

Abstract

fetched live from OpenAlex

Abstract New dating of lahar-killed trees underscores volcano hazards in the Puget Sound metropolitan area. Beginning as a landslide from the west flank of Mount Rainier, Washington, USA, the Electron Mudflow, which was the largest lahar of the last millennium, swept more than 60 km down the Puyallup River drainage into areas now densely populated. Wiggle matching of seven radiocarbon ages from buried, bark-bearing Douglas-fir (Pseudotsuga menziesii) trees brackets the mudflow's age between 1477 and 1522 CE with 99.7% certainty. To narrow this date, we applied dendrochronology crossdating on samples collected from 21 trees killed by the lahar, measuring 86 time series for statistical verification. The four bark-bearing trees died the same year while the final rings in all other trees had decayed, exposing rings formed in earlier years. When averaged together, the crossdated measurements form a 475 yr master chronology that was correlated against absolutely dated tree-ring chronologies in the region. The Electron chronology best matched with chronologies from low-elevation sites, especially a Douglas-fir chronology from Vancouver Island, Canada, to show that the Electron trees died in 1507 CE. Latewood in the final ring was beginning to form, indicating the mudflow likely occurred in the late-summer months. What caused the Electron Mudflow is unknown, but this precise date will help to assess possible relationships with other events, assist in interpreting Indigenous narratives about the mudflow, and increase awareness of potential lahar hazards.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.307
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.003
GPT teacher head0.197
Teacher spread0.195 · 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 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

Citations1
Published2025
Admission routes1
Has abstractyes

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