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Record W4210472897 · doi:10.1002/essoar.10510387.2

1.2-million-year band of Earth--Mars obliquity modulation on the evolution of cold late Miocene to warm early Pliocene climate

2022· preprint· en· W4210472897 on OpenAlexaff
Jie Qin, Rui Zhang, Vadim A. Kravchinsky, Jean‐Pierre Valet, Leonardo Sagnotti, Jianxing Li, Xu Yong, Taslima Anwar, Leping Yue

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldPhysics and Astronomy
TopicPlanetary Science and Exploration
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsGeologyGlobal coolingAridificationLate MiocenePaleontologyNorthern HemisphereCenozoicPaleoclimatologyMonsoonGlacial periodEast Asian MonsoonClimate changeNeogeneClimatologyOceanographyStructural basin

Abstract

fetched live from OpenAlex

The climatic transitions during the Miocene–Pliocene epochs had significant impacts on the worldwide biological diversity and were associated with large turnovers of continental vegetation and fauna. Previous studies have shown that late Miocene cooling and continental aridification which was initiated 7 Ma reversed to warm conditions across the Miocene–Pliocene Boundary ~ 5.3 Ma. Here we present detailed orbital pacing of Asian monsoon deposits to constrain further the global climate change during this period. We produce high-resolution magnetic susceptibility records which reveal that the 1.2 Myr obliquity modulation would have been the main driving factor of the cooling and warming that occurred ~ 7 Ma and 5.3 Ma, respectively. The Tibetan rise and closures of the Panama and Indonesian seaways enhanced the impact of the 405 kyr eccentricity cycles to an oscillatory climatic state while the Northern Hemisphere glaciations were increasing from 4 to 2.5 Ma.

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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.003
Threshold uncertainty score0.011

Distilled classifier scores by category (both heads)

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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.019
GPT teacher head0.233
Teacher spread0.214 · 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 designSimulation or modeling
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
Published2022
Admission routes1
Has abstractyes

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