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Record W4379599040 · doi:10.1021/acs.est.2c09595

Broaden Research on Ocean Alkalinity Enhancement to Better Characterize Social Impacts

2023· review· en· W4379599040 on OpenAlex
Sara Nawaz, Javier Lezaun, José María Valenzuela, Phil Renforth

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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueEnvironmental Science & Technology · 2023
Typereview
Languageen
FieldEarth and Planetary Sciences
TopicOcean Acidification Effects and Responses
Canadian institutionsUniversity of British Columbia
FundersEuropean CommissionH2020 EnvironmentPacific Institute for Climate Solutions
KeywordsAlkalinityScale (ratio)Environmental scienceEnvironmental resource managementEnvironmental planningAtmosphere (unit)Natural resource economicsOcean acidificationEnvironmental economicsBusinessRisk analysis (engineering)Climate changeOceanographyMeteorologyGeographyEconomicsGeologyChemistry

Abstract

fetched live from OpenAlex

Ocean alkalinity enhancement (OAE) is being considered as a way of achieving large-scale removals of carbon dioxide from the atmosphere. Research on the risks and benefits of different OAE approaches is expanding apace, but it remains difficult to anticipate and appraise the potential impacts to human communities that OAE might generate. These impacts, however, will be critical to evaluating the viability of specific OAE projects. This paper draws on the authors' involvement in interdisciplinary assessment of OAE (1) to identify the factors that currently limit characterization of potential social impacts and (2) to propose ways of reconfiguring OAE research to better consider these.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.998
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.003
Science and technology studies0.0010.002
Scholarly communication0.0000.000
Open science0.0020.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.014

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 head0.103
GPT teacher head0.376
Teacher spread0.273 · 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