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Record W4408428434 · doi:10.5194/egusphere-egu25-14364

Hydrogeological challenges for carbon mineralization in terrestrial mafic/ultramafic rock bodies

2025· preprint· en· W4408428434 on OpenAlexaff
Kent Novakowski, Greg Maidment, C. Sanchez Roa

Bibliographic record

Venuenot available
Typepreprint
Languageen
FieldEnvironmental Science
TopicMethane Hydrates and Related Phenomena
Canadian institutionsCentrale des Syndicats du QuébecQueen's University
Fundersnot available
KeywordsMaficUltramafic rockMineralization (soil science)GeochemistryGeologyHydrogeologySoil scienceGeotechnical engineeringSoil water

Abstract

fetched live from OpenAlex

The widespread and substantive occurrence of basalt in ocean basins has long been recognised as a potential reservoir for CO2 removal via mineralization having stability over extended periods of geological time. Due to the significant cost of fully exploring this potential, more recent attention has focused on geochemically equivalent rocks in more accessible terrestrial terranes. Examples occur in ophiolite sequences, terrestrial volcanic environments, and even in stable cratonic settings that have undergone considerable metamorphism. There have been abundant studies of the hydrogeology of crystalline rock in general by the nuclear waste and mining industries, and for water supply, which clearly illustrate that flow and transport are governed in these rock types by a sparse network of discrete fractures having relatively small aperture (10s to a few 100 μm with a total void volume of

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.005
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.034
GPT teacher head0.263
Teacher spread0.229 · 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 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

Citations0
Published2025
Admission routes1
Has abstractyes

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