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The Verification Challenge of Marine Carbon Dioxide Removal

2025· review· en· W4412977212 on OpenAlexaff
Katja Fennel

Bibliographic record

VenueAnnual Review of Marine Science · 2025
Typereview
Languageen
FieldEnergy
TopicGlobal Energy and Sustainability Research
Canadian institutionsDalhousie University
Fundersnot available
KeywordsAtmosphere (unit)SeawaterCarbon dioxideEnvironmental scienceCarbon fibersFlux (metallurgy)Carbon dioxide removalCarbon fluxIntervention (counseling)Environmental engineeringOceanographyChemistryComputer scienceMeteorologyEcologyGeologyEcosystemBiology

Abstract

fetched live from OpenAlex

It is increasingly obvious that, even when reaching net-zero emissions, removal of anthropogenic CO 2 from the atmosphere will be required. Some ocean-based removal technologies, while not proven for routine operation at scale, show promise. All of these rely on inducing a flux of CO 2 from the atmosphere into the ocean that is directly attributable to the removal intervention. Crucial for the economic viability of these technologies is the quantification of the cumulative net air–sea flux of CO 2 that an intervention can verifiably deliver. Because this flux is the difference between a realistic case with and a hypothetical case without intervention, it cannot be determined by observation alone—one must rely on a combination of informative observations and skillful models. Major uncertainties in the quantification of net CO 2 uptake include the removal of seawater with a dissolved inorganic carbon deficit from direct contact with the atmosphere and the inevitable rebalancing of carbon among Earth's mobile carbon pools.

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: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.005
Threshold uncertainty score0.015

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0020.003
Open science0.0010.001
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0050.002

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 head0.022
GPT teacher head0.346
Teacher spread0.324 · 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
GenreReview

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

Citations6
Published2025
Admission routes1
Has abstractyes

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