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Record W4387540266 · doi:10.1130/g51725.1

Ice-rafted dropstones at midlatitudes in the Cretaceous of continental Iberia

2023· article· en· W4387540266 on OpenAlexaboutno aff
Juan Pedro Rodríguez‐López, Carlos L. Liesa, Aránzazu Luzón, Arsenio Muñoz Jiménez, María José Mayayo Burillo, Julian B. Murton, Ana R. Soria

Bibliographic record

VenueGeology · 2023
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsnot available
FundersUniversidad de ZaragozaEuskal Herriko UnibertsitateaAgencia Estatal de InvestigaciónEusko Jaurlaritza
KeywordsGeologyIcebergGlacierGlacial periodPaleontologyIce sheetOceanographyArcticPleistoceneContinental shelfCretaceousPhysical geographyGeography

Abstract

fetched live from OpenAlex

Abstract The Cretaceous is widely considered to have been a period subjected to super-greenhouse conditions. Here, we provide multiscale sedimentologic evidence of glaciers developing at mid-paleolatitudes (~45°N) in continental Iberia during the Hauterivian cold snap. Striated and faceted ice-rafted glacial dropstones (cobble to boulder size) and striated and grooved silt- to sand-sized grains (ice-rafted debris [IRD]) occur in a lacustrine sequence of the Enciso Group in the eastern Cameros Basin, Spain. The ice-rafted materials constitute the first evidence reported for a Cretaceous continental cryospheric record in Europe, and they are attributed to calving of glacier snouts, releasing icebergs into an ice-contact lake. The sedimentary succession resembles glacial-deglacial records in lakes overridden by the late Pleistocene Laurentide Ice Sheet in eastern Arctic Canada. The Iberian glacial succession was coeval with plateau permafrost in Asia and IRD records in the Arctic and Australia, revealing a stronger than previously thought cryosphere during the global Hauterivian cold snap.

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 categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.036
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.002

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 head0.021
GPT teacher head0.259
Teacher spread0.238 · 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; both teacher heads agree on what is shown here.

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

Citations7
Published2023
Admission routes1
Has abstractyes

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