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Record W7083711891 · doi:10.5066/p13wavxh

Radiocarbon and tree-ring data for dating Mount Rainier's Electron Mudflow

2025· dataset· en· W7083711891 on OpenAlexaboutno aff

Bibliographic record

VenueUSGS DOI Tool Production Environment · 2025
Typedataset
Languageen
FieldEarth and Planetary Sciences
TopicGeological and Geochemical Analysis
Canadian institutionsnot available
Fundersnot available
KeywordsChronologyMudflowRadiocarbon datingAbsolute datingRadiometric datingLandslide

Abstract

fetched live from OpenAlex

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, the Electron Mudflow swept more than 60 km down the Puyallup River drainage into areas now heavily populated. Wiggle-matching of seven radiocarbon ages from buried, bark-bearing Douglas-fir (Pseudotsuga menziesii) trees brackets the mudflow’s age between 1477–1522 CE with 99.7 percent certainty. The trees with bark all died the same year. Analysis of the Electron chronology using crossdating of tree rings 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. These data are in three tables, containing properties of the lahar-killed Douglas-fir included in the Electron tree-ring chronology (C14_S1.csv); radiocarbon data from trees killed in the Electron Mudflow (C14_S2.csv); and properties of long tree-ring chronologies in Oregon, Washington, and southern British Columbia and their relationship to the Electron chronology (C14_S3.csv). These are supplementary data tables to support the larger work by Black et al. 2025. Data users are strongly encouraged to refer to that larger work for additional details.

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 categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.040
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 science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.015
GPT teacher head0.211
Teacher spread0.196 · 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
GenreDataset

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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