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Sociotechnical Considerations About Ocean Carbon Dioxide Removal

2022· review· en· W4285801145 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueAnnual Review of Marine Science · 2022
Typereview
Languageen
FieldEarth and Planetary Sciences
TopicOcean Acidification Effects and Responses
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsSociotechnical systemCarbon sequestrationCentralityGlobal warmingEarth system scienceClimate changeEnvironmental scienceCarbon dioxide in Earth's atmosphereEnvironmental resource managementEnvironmental economicsCarbon dioxideComputer scienceOceanographyEcologyEconomicsGeology

Abstract

fetched live from OpenAlex

Ocean carbon dioxide removal (OCDR) is rapidly attracting interest, as climate change is putting ecosystems at risk and endangering human communities globally. Due to the centrality of the ocean in the global carbon cycle, augmenting the carbon sequestration capacity of the ocean could be a powerful mechanism for the removal of legacy excess emissions. However, OCDR requires careful assessment due to the unique biophysical characteristics of the ocean and its centrality in the Earth system and many social systems. Using a sociotechnical system lens, this review identifies the sets of considerations that need to be included within robust assessments for OCDR decision-making. Specifically, it lays out the state of technical assessments of OCDR approaches along with key financial concerns, social issues (including public perceptions), and the underlying ethical debates and concerns that would need to be addressed if OCDR were to be deployed as a carbon dioxide removal strategy.

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.

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)0.004
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.988
Threshold uncertainty score0.995

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.002
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0000.001
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 head0.031
GPT teacher head0.315
Teacher spread0.284 · 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