MétaCan
Menu
Back to cohort
Record W4393990940 · doi:10.1029/2024ef004496

Acceleration of Deep Subsurface Fluid Fluxes in the Anthropocene

2024· article· en· W4393990940 on OpenAlexafffund
Grant Ferguson, Lydia Bailey, Ji‐Hyun Kim, Magdalena R. Osburn, Peter W. Reiners, Henrik Drake, Bradley S. Stevenson, Jennifer C. McIntosh

Bibliographic record

VenueEarth s Future · 2024
Typearticle
Languageen
FieldEnvironmental Science
TopicCO2 Sequestration and Geologic Interactions
Canadian institutionsUniversity of Saskatchewan
FundersNatural Sciences and Engineering Research Council of CanadaNational Science Foundation
KeywordsAnthropoceneAccelerationGeologyEarth scienceEnvironmental scienceAstrobiologyPetroleum engineeringPaleontologyPhysics

Abstract

fetched live from OpenAlex

Abstract The Anthropocene has been framed around humanity's impact on atmospheric, biologic, and near‐surface processes, such as land use and vegetation change, greenhouse gas emissions, and the above‐ground hydrologic cycle. Groundwater extraction has lowered water tables in many key aquifers but comparatively little attention has been given to the impacts in the deeper subsurface. Here, we show that fluid fluxes from the extraction and injection of fluids associated with oil and gas production and inflow of water into mines likely exceed background flow rates in deep (>500 m) groundwater systems at a global scale. Projected carbon capture and sequestration (CCS), geothermal energy production, and lithium extraction to facilitate the energy transition will require fluid production rates exceeding current oil and co‐produced water extraction. Natural analogs and geochemical modeling indicate that subsurface fluid manipulation in the Anthropocene will likely appear in the rock record. The magnitude and importance of these changes are unclear, due to a lack of understanding of how deep subsurface hydrologic and geochemical cycles and associated microbial life interact with the rest of the Earth system.

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)0.000
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 score0.453
Threshold uncertainty score0.993

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0080.000

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 head0.009
GPT teacher head0.256
Teacher spread0.247 · 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
Published2024
Admission routes2
Has abstractyes

Explore more

Same venueEarth s FutureSame topicCO2 Sequestration and Geologic InteractionsFrench-language works237,207