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Record W2295764186 · doi:10.1130/g37588.1

Can CO<sub>2</sub>trigger a thermal geyser eruption?

2016· article· en· W2295764186 on OpenAlexafffund
Bethany Ladd, M. Cathryn Ryan

Bibliographic record

VenueGeology · 2016
Typearticle
Languageen
FieldEnvironmental Science
TopicAtmospheric and Environmental Gas Dynamics
Canadian institutionsUniversity of Calgary
FundersNatural Sciences and Engineering Research Council of CanadaNorthwestern University
KeywordsGeologyThermalSeismologyMeteorologyGeography

Abstract

fetched live from OpenAlex

Geyser eruptions are produced by a complex and poorly understood set of subsurface processes and conditions. They typically have an abundant supply of water, relatively permeable and competent subsurface material, a conduit to the surface, a driving mechanism (commonly believed to be the initiation of gas lift pumping by steam formation in the conduit), and a trigger. Here we present time series of dissolved CO 2 concentrations in near-surface discharge waters of a thermal geyser in Yellowstone National Park (northwestern United States) that vary systematically over several eruption cycles. Chemical geothermometry, combined with a temperature profile in a nearby well, suggests that the geyser water ascends from non-boiling conditions (∼153–171 °C at a depth of 57–65 m). When the time series of near-surface measured CO 2 concentrations are extrapolated to these subsurface conditions assuming dominantly adiabatic cooling, the additional gas pressure from dissolved CO 2 is large enough to cause the total dissolved gas pressure to exceed bubbling pressure, inducing bubble formation. We postulate that CO 2 is a necessary component to triggering eruptions in the geyser studied. Furthermore, unlike steam, CO 2 bubbles do not completely re-condense during cooling in the geyser conduit, hence providing better sustenance for gas lift pumping than pure H 2 O boiling.

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 categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.324
Threshold uncertainty score0.997

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 studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0050.004

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 head0.005
GPT teacher head0.192
Teacher spread0.187 · 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; both teacher heads agree on what is shown here.

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

Citations22
Published2016
Admission routes2
Has abstractyes

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