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Record W2996059436 · doi:10.1002/jqs.3176

Dawson tephra, a widespread 29‐ka marker bed, in a marine core from Patton Seamount off the Alaska Peninsula and its potential marine–terrestrial correlation

2019· article· en· W2996059436 on OpenAlexaboutno aff
Kaori Aoki

Bibliographic record

VenueJournal of Quaternary Science · 2019
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsnot available
FundersMinisterio de Educación, Cultura y DeporteBundesministerium für Bildung und ForschungMinistry of Education, Culture, Sports, Science and Technology
KeywordsTephraGeologyStadialSeamountVolcanoIce coreOceanographyMarine isotope stagePleistocenePaleontologyInterglacial

Abstract

fetched live from OpenAlex

ABSTRACT A tephra layer with normal grading in the sub‐bottom depth interval 119–122 cm in marine core SO202‐27‐6 was collected on Patton Seamount in the northeast North Pacific Ocean. Based on the geochemistry of volcanic glass shards determined by a wavelength dispersive electron probe micro‐analyser and an X‐ray fluorescence analyser, this layer is correlated to the Dawson tephra, a widespread late Pleistocene time marker tephra in Alaska and the Yukon. The age of the Dawson tephra in the core is 29.03 ± 0.178 ka (1 sigma) based on a published age model. The Dawson tephra is revealed to have been deposited in the transition from marine isotope stage 3 to 2, i.e. the last stage of Heinrich Stadial 3 derived from the ice‐rafted debris signal. According to the correlation between Greenland (NGRIP ice core) and this core, the Dawson tephra occupies the record immediately before inter stadial 4 in the δ 18 O stratigraphy of NGRIP. The Dawson tephra on Patton Seamount includes lithic fragments, which suggests that it was deposited not only by fall‐out but also in part via another mechanism, such as icebergs from the Cordilleran ice sheet or seasonal sea ice.

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)0.002
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 score0.098
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0020.000

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 head0.018
GPT teacher head0.250
Teacher spread0.232 · 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

Citations6
Published2019
Admission routes1
Has abstractyes

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