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Record W4406273270 · doi:10.1038/s41598-025-86042-7

Mitigating anthropogenic climate change with aqueous green energy

2025· article· en· W4406273270 on OpenAlexafffund
Sophia T Olim, Anna G Nickoloff, Leslie J. Moffat, Andrew J. Weaver, Michael Eby

Bibliographic record

VenueScientific Reports · 2025
Typearticle
Languageen
FieldEngineering
TopicCarbon Dioxide Capture Technologies
Canadian institutionsUniversity of Victoria
FundersNatural Sciences and Engineering Research Council of CanadaGovernment of Canada
KeywordsEnvironmental scienceElectricityOcean thermal energy conversionGlobal warmingLimitingClimate changeGreenhouse gasCoalElectricity generationThermal energyCarbon capture and storage (timeline)Waste managementOceanographyPower (physics)SeawaterEngineering

Abstract

fetched live from OpenAlex

Abstract Reaching net zero emissions and limiting global warming to 2 °C requires the widespread introduction of technology-based solutions to draw down existing atmospheric levels and future emissions of CO 2 . One such approach is direct air CO 2 capture and storage (DACCS), a readily available, yet energy-intensive process. The combination of DACCS and ocean thermal energy conversion (OTEC) allows for independently powered carbon capture plants to inject concentrated carbon into deep marine sediments where storage is generally safe and permanent. OTEC is a form of electricity production that exploits the temperature difference between deep and shallow ocean waters, and can power DACCS on floating platforms at a price competitive with coal-generated electricity. Here we highlight the scale of the challenge facing society. We show that a safe and sustainable level of OTEC-generated electricity powering DACCS for 70 years could result in up to a 35% decrease in the relative global mean temperature warming compared to a business-as-usual emissions scenario.

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 categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.503
Threshold uncertainty score0.623

Codex and Gemma teacher scores by category

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

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 head0.009
GPT teacher head0.213
Teacher spread0.204 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations7
Published2025
Admission routes2
Has abstractyes

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