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Record W4251946490 · doi:10.1002/essoar.10503106.1

Irreversibility of Marine Climate Change Impacts under Carbon Dioxide Removal

2020· preprint· en· W4251946490 on OpenAlexafffund
Xinru Li, Kirsten Zickfeld, Sabine Mathesius, Karen E. Kohfeld, J. B. Matthews

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEarth and Planetary Sciences
TopicOcean Acidification Effects and Responses
Canadian institutionsSimon Fraser UniversityMemorial University of NewfoundlandUniversity of British Columbia
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsWorld Wide WebClimate changeOceanographyLibrary scienceComputer scienceGeology

Abstract

fetched live from OpenAlex

Artificial carbon dioxide removal (CDR) from the atmosphere has been proposed as a measure for mitigating climate change and restoring the climate system to a target state after exceedance (“overshoot”). This research investigates to what extent overshoot and subsequent recovery of a given cumulative CO2 emissions level by CDR leaves a legacy in the marine environment using an Earth system model. We use RCP2.6 and its extension to year 2300 as the reference scenario and design a set of cumulative emissions and temperature overshoot scenarios based on other RCPs. Our results suggest that the overshoot and subsequent return to a reference cumulative emissions level would leave substantial impacts on the marine environment. Although the changes in sea surface temperature, pH and dissolved oxygen are largely reversible, global mean values and spatial patterns of these variables differ significantly from those in the reference scenario when the reference cumulative emissions are attained.

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)0.001
metaresearch head score (Gemma)0.002
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 score0.013
Threshold uncertainty score0.025

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.000
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.261
Teacher spread0.212 · 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

Citations2
Published2020
Admission routes2
Has abstractyes

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