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Record W2138894985 · doi:10.1130/g31208.1

Early to Middle Miocene monsoon climate in Australia

2010· article· en· W2138894985 on OpenAlexaff
Nicholas Herold, Matthew Huber, David R. Greenwood, R. Dietmar Müller, Maria Seton

Bibliographic record

VenueGeology · 2010
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsBrandon University
Fundersnot available
KeywordsGeologyMonsoonClimatologyOceanographyEast Asian MonsoonPaleontology

Abstract

fetched live from OpenAlex

The present-day Australian monsoon delivers substantial moisture to the northern regions of a predominantly arid continent. However, the pre-Quaternary history of the Australian monsoon is poorly constrained due to sparse and often poorly dated paleoclimate proxy evidence. Sedimentological and paleontological data suggest that warm, humid, and seasonal environments prevailed in central and north Australia during the Miocene, though it is unclear whether these were products of the Australian monsoon. We perform a series of sensitivity experiments using an atmospheric general circulation model, combined with an offline equilibrium vegetation model, to quantitatively constrain the areal extent of the Miocene monsoon. Our results suggest a weaker than modern monsoon climate during the Miocene. This result is insensitive to atmospheric CO2, although somewhat sensitive to vegetation interactions and the presumed distribution of inland water bodies. None of our Miocene experiments exhibit precipitation rates greater than modern over north Australia, in disagreement with paleoclimate record interpretations. Vegetation modeling indicates that inferred precipitation values from fossil flora and fauna could only support Miocene vegetation patterns if atmospheric CO2 was twice the modern concentration. This suggests that elevated CO2 was critical for sustaining Miocene vegetation.

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)0.000
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 score0.107
Threshold uncertainty score0.992

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0090.010

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 head0.037
GPT teacher head0.274
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

Citations69
Published2010
Admission routes1
Has abstractyes

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