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Record W2976001049 · doi:10.1038/s41467-019-12357-5

Low CO2 levels of the entire Pleistocene epoch

2019· article· en· W2976001049 on OpenAlexaff
Jiawei Da, Yige Zhang, Gen Li, Xianqiang Meng, Junfeng Ji

Bibliographic record

VenueNature Communications · 2019
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsMinistry of Education and Child Care
FundersNanjing UniversityNational Natural Science Foundation of China
KeywordsPaleosolPleistoceneIce coreProxy (statistics)LoessClimate changePlateau (mathematics)GeologyLoess plateauPhysical geographyIce agePaleoclimatologyGlacial periodAtmospheric sciencesClimatologyPaleontologySoil scienceGeographyOceanography

Abstract

fetched live from OpenAlex

Abstract Quantifying ancient atmosphericpCO2provides valuable insights into the interplay between greenhouse gases and global climate. Beyond the 800-ky history uncovered by ice cores, discrepancies in both the trend and magnitude ofpCO2changes remain among different proxy-derived results. The traditional paleosolpCO2paleobarometer suffers from largely unconstrained soil-respired CO2concentration (S(z)). Using finely disseminated carbonates precipitated in paleosols from the Chinese Loess Plateau, here we identified that theirS(z) can be quantitatively constrained by soil magnetic susceptibility. Based on this approach, we reconstructedpCO2during 2.6–0.9 Ma, which documents overall lowpCO2levels (<300 ppm) comparable with ice core records, indicating that the Earth system has operated under late PleistocenepCO2levels for an extended period. ThepCO2levels do not show statistically significant differences across the mid-Pleistocene Transition (ca. 1.2–0.8 Ma), suggesting that CO2is probably not the driver of this important climate change event.

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 score0.010
Threshold uncertainty score0.020

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 communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.028
GPT teacher head0.286
Teacher spread0.258 · 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

Citations72
Published2019
Admission routes1
Has abstractyes

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