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Record W4320914117 · doi:10.1029/2022gl102020

A Positive Cooling Feedback for the Neoproterozoic Snowball Earth Initiation Due To Weakening of Ocean Ventilation

2023· article· en· W4320914117 on OpenAlexaff
Peng Liu, Yonggang Liu, Sifan Gu, Paul F. Hoffman, Sanzhong Li

Bibliographic record

VenueGeophysical Research Letters · 2023
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicGeology and Paleoclimatology Research
Canadian institutionsUniversity of Victoria
Fundersnot available
KeywordsDeep seaSnowball EarthGlacial periodVentilation (architecture)GeologyIce ageAtmosphere (unit)Sea levelEnvironmental scienceAtmospheric sciencesClimatologyOceanographyMeteorologyPaleontologyGeography

Abstract

fetched live from OpenAlex

Abstract Ocean ventilation is an important regulator for atmospheric CO 2 level ( p CO 2 ) by affecting the relative proportion of carbon stored in the atmosphere and deep ocean. Expansion of sea ice during glacial periods slows down ocean ventilation and its effect is expected to be the largest during the Neoproterozoic pre‐snowball stage. Here, our Community Earth System Model version 1.2.2 simulations demonstrate that averaged deep ocean ventilation age almost triples when the climate cools from a warm state with negligible sea ice to one in which the global sea‐ice coverage reaches ∼50% when p CO 2 is lowered to 280 ppmv. Further cooling by reducing p CO 2 from 280 to 70 ppmv increases the ventilation age from 1900 to 2300 years. This latter small increase in deep‐ocean ventilation age can reduce p CO 2 by 48 ppmv, assuming Neoproterozoic organic production was comparable to present level. Therefore, the weakened ocean ventilation constitutes a significant positive feedback to the Late Neoproterozoic climate cooling.

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)0.001
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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.010
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0060.000

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 head0.049
GPT teacher head0.315
Teacher spread0.265 · 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

Citations4
Published2023
Admission routes1
Has abstractyes

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