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Record W2655300270 · doi:10.1002/2017gl074369

Multistage 8.2 kyr event revealed through high‐resolution XRF core scanning of Cuban sinkhole sediments

2017· article· en· W2655300270 on OpenAlexafffund
Matthew Peros, Shawn Collins, Anna Agosta G'Meiner, Eduard G. Reinhardt, Felipe Matos Pupo

Bibliographic record

VenueGeophysical Research Letters · 2017
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsMcMaster UniversityMcGill UniversityBishop's University
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsAssociation for Applied Sport Psychology
KeywordsSinkholeGeologyUpwellingOceanographySalinityPaleontology

Abstract

fetched live from OpenAlex

Abstract We use sediments from a flooded sinkhole (Cenote Jennifer) in northern Cuba to provide new, well‐dated, high‐resolution evidence for the 8.2 kyr event. From ~7600 to 8700 cal yr B.P. the sinkhole contained shallow, low‐salinity water, which supported a marsh dominated by cattail and grass. Peaks in Cl and Br—occurring at 8150, 8200, and 8250 cal yr B.P.—are attributable to increased evaporation due to regional drying associated with the 8.2 kyr event. The three peaks in these elements also closely correspond to the greyscale record from the Cariaco Basin, indicative of increased upwelling in the southern Caribbean Sea at this time, supporting the notion of a multistage 8.2 kyr event. Our work provides new data that help to clarify the initiation, behavior, and impacts of the 8.2 kyr event in the northern tropics.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.089
Threshold uncertainty score0.177

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
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 head0.075
GPT teacher head0.356
Teacher spread0.282 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations32
Published2017
Admission routes2
Has abstractyes

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