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Record W4406128139 · doi:10.3389/fclim.2024.1506181

Perspectives and challenges of marine carbon dioxide removal

2025· article· en· W4406128139 on OpenAlexaff
Andreas Oschlies, Lennart T. Bach, Katja Fennel, Jean‐Pierre Gattuso, Nadine Mengis

Bibliographic record

VenueFrontiers in Climate · 2025
Typearticle
Languageen
FieldEarth and Planetary Sciences
TopicOcean Acidification Effects and Responses
Canadian institutionsDalhousie University
FundersHORIZON EUROPE Framework ProgrammeBundesministerium für Bildung und ForschungEuropean Commission
KeywordsEnvironmental scienceGreenhouse gasCarbon dioxideSeawaterEnvironmental resource managementBusinessEnvironmental protectionNatural resource economicsOceanographyEcologyBiologyGeology

Abstract

fetched live from OpenAlex

The Paris Agreement to limit global warming to well below 2°C requires drastic reductions in greenhouse gas emissions and the balancing of any remaining emissions by carbon dioxide removal (CDR). Due to uncertainties about the potential and durability of many land-based approaches to deliver sufficient CDR, marine CDR options are receiving more and more interest. We present the current state of knowledge regarding the potentials, risks, side effects as well as challenges associated with technical feasibility, governance, monitoring, reporting and accounting of marine CDR, covering a range of biotic and geochemical approaches. We specifically discuss to what extent a comparison with direct injection of CO 2 into seawater, which had been proposed decades ago and is now prohibited by international agreements, may provide guidance for evaluating some of the biotic marine CDR approaches.

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)0.005
metaresearch head score (Gemma)0.004
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.008
Threshold uncertainty score0.024

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0050.004
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0010.004
Scholarly communication0.0050.004
Open science0.0010.003
Research integrity0.0030.003
Insufficient payload (model declined to judge)0.0040.000

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 head0.011
GPT teacher head0.225
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 designNot applicable
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

Citations37
Published2025
Admission routes1
Has abstractyes

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