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Record W4402357056 · doi:10.1186/s40562-024-00357-3

A snapshot of the climate in the Middle Pleistocene inferred from a stalagmite from central Japan

2024· article· en· W4402357056 on OpenAlexaff
M. Sakai, Masako Hori, Ryu Uemura, Bassam Ghaleb, Daniele L. Pinti, Mahiro Yumiba, Masafumi Murayama, Akihiro Kano

Bibliographic record

VenueGeoscience Letters · 2024
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsUniversité du Québec à Montréal
FundersJapan Society for the Promotion of Science
KeywordsStalagmiteGeologyPleistoceneInterglacialSpeleothemCavePaleoclimatologyGlacial periodPhysical geographyClimatologyPaleontologyClimate changeOceanographyGeographyHoloceneArchaeology

Abstract

fetched live from OpenAlex

Abstract Stalagmites are useful archives in reconstructing paleoclimates: most paleoclimate studies used stalagmites that are distributed in specific locations and ages. We examined a stalagmite (GYM-1) collected from Nara Prefecture, central Japan, where limestone areas are limited. Applying 238 U– 234 U method, the ages of GYM-1 were determined as 744 ± 70 to 677 ± 74 ka (based only on analytical uncertainties, 1σ). Even assuming a 10% uncertainty in the initial activity of 234 U/ 238 U, ( 234 U/ 238 U) 0 , this age could be still older than 460 ka. Temperatures calculated based on δD in the fluid inclusions and δ 18 O in the calcium carbonate ranged from 9.0 to 11.9 °C (10.8 ± 0.9 °C on average) or from 6.0 to 9.1 °C (7.9 ± 0.9 °C on average) depending on the equation. The estimated temperature suggests that GYM-1 formed during an interglacial period of the Middle Pleistocene. Synchronous behavior of isotopic values with lamination likely reflects seasonal temperature in a highly ventilated cave system.

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)0.001
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 score0.018
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
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 head0.027
GPT teacher head0.227
Teacher spread0.200 · 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

Citations0
Published2024
Admission routes1
Has abstractyes

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