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Record W2473060411 · doi:10.1002/2016gc006400

Late‐Pleistocene precipitation δ<sup>18</sup>O interpolated across the global landmass

2016· article· en· W2473060411 on OpenAlexaff
Scott Jasechko

Bibliographic record

VenueGeochemistry Geophysics Geosystems · 2016
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsPleistoceneGlacial periodGeologyHoloceneInterglacialNorthern HemispherePrecipitationPhysical geographyDeglaciationClimate changeIce sheetEarly PleistocenePaleontologyClimatologyOceanographyGeography

Abstract

fetched live from OpenAlex

Abstract Global water cycles, ecosystem assemblages, and weathering rates were impacted by the ∼4°C of global warming that took place over the course of the last glacial termination. Fossil groundwaters can be useful indicators of late‐Pleistocene precipitation isotope compositions, which, in turn, can help to test hypotheses about the drivers and impacts of glacial‐interglacial climate changes. Here, a global catalog of 126 fossil groundwater records is used to interpolate late‐Pleistocene precipitation δ 18 O across the global landmass. The interpolated data show that extratropical late‐Pleistocene terrestrial precipitation was near uniformly depleted in 18 O relative to the late Holocene. By contrast, tropical δ 18 O responses to deglacial warming diverged; late‐Pleistocene δ 18 O was higher‐than‐modern across India and South China but lower‐than‐modern throughout much of northern and southern Africa. Groundwaters that recharged beneath large northern hemisphere ice sheets have different Holocene‐Pleistocene δ 18 O relationships than paleowaters that recharged subaerially, potentially aiding reconstructions of englacial transport in paleo ice sheets. Global terrestrial late‐Pleistocene precipitation δ 18 O maps may help to determine 3‐D groundwater age distributions, constrain Pleistocene mammal movements, and better understand glacial climate dynamics.

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 score0.035
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
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.002

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 head0.014
GPT teacher head0.251
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

Citations26
Published2016
Admission routes1
Has abstractyes

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