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Record W4415175319 · doi:10.70212/cdrxiv.2025457.v1

Lethal by design? Guiding environmental assessments of ocean alkalinity enhancement toward realistic contextualization of the alkalinity perturbation

2025· preprint· en· W4415175319 on OpenAlexfundno aff
Lennart T. Bach, Michael D. Tyka, Bin Wang, Katja Fennel

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEarth and Planetary Sciences
TopicOcean Acidification Effects and Responses
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsAlkalinityContextualizationSeawaterPerturbation (astronomy)Context (archaeology)Carbonate

Abstract

fetched live from OpenAlex

Ocean Alkalinity Enhancement (OAE) aims to mitigate climate change by increasing the chemical capacity of seawater to store anthropogenic CO2. OAE can be implemented through multiple pathways, each of which intentionally modifies marine carbonate chemistry through increases in total alkalinity (TA). Experimental research has only recently begun to assess how such TA perturbations (∆TA) affect ocean geochemical processes and ecosystems. Meaningful assessments need context on how ∆TA induced by different OAE pathways would evolve over time and in magnitude. Here, we use a dilution equation, a regional model, and a global model to explore how marine systems and life styles would experience ∆TA under realistic constraints. We find that a more extreme ∆TA of >1000 μmol kg-1, a perturbation commonly considered in OAE experiments, only occurs for minutes in a miniscule fraction of the OAE-perturbed seawater volume. In contrast, ∆TA between 1-100 μmol kg-1 is a ubiquitous perturbation range for OAE under real-world constraints, yet rarely in focus of environmental OAE assessments. These results suggest that there is a disconnect between real-world ∆TA that can plausibly be invoked by OAE and the experimental ∆TA range frequently used in the context of the environmental OAE assessment. While “unrealistic” ∆TA can provide crucial insights into response patterns to OAE, they can also cause overestimation of OAE effects, if the unrealistic ∆TA is not contextualized appropriately. Our results can be used to improve the contextualization of OAE studies, thereby making the interpretation of ∆TA effects on the environment more robust.

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)0.001
metaresearch head score (Gemma)0.000
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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.305
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.055
GPT teacher head0.286
Teacher spread0.231 · 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.

Study designObservational
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

Citations4
Published2025
Admission routes1
Has abstractyes

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